Inverse Approach Research Articles

How do active road users act around autonomous vehicles? An inverse reinforcement learning approach

The inevitable impact of autonomous vehicles (AV) on traffic safety is becoming a reality with the progressive deployment of these vehicles in different parts of the world. Still, many questions linger in the minds of road users that will share the road and interact with these AVs on a daily basis. To answer some of these questions, this study utilized recently collected real-world AV data from the United States, with the focus mainly targeting active road users. Specifically, the 1,492 h of recorded trips were processed to extract AV-pedestrian and AV-cyclist interactions of different movement types. The interactions were then investigated to gain a better understanding of the active road users’ behavior, while excluding any interactions that involved intervention from the AVs’ human test drivers. Through deep maximum entropy inverse reinforcement learning (DME-IRL), the reward functions describing the utility of active road users were retrieved and assessed for five interaction scenarios, including parallel, opposing, crossing, and turning (left and right) interactions. In addition, the policies developed as part of the solution were used to simulate the behavior of active road users and validate the resulting conflicts in terms of safety and evasive actions. Overall, the utilized approach demonstrated high accuracy in mimicking the interaction behavior of active road users when encountering an AV, with 81–84% accuracy in predicting evasive actions in parallel and opposing interactions and 12–17% mean absolute error for safety indicators in crossing and turning interactions. In addition, the resulting reward functions provided reliable insight onto the preferences and considerations of the active road users in these situations. Overall, cyclists tend to be less cautious around AVs compared to pedestrians, while the AVs tend to slow down and leave sufficient distance from all active road users in most cases. Still, the robotic behavior of the AVs, which can sometimes be inconsistent, leads to risky evasive actions by the active road users, which can affect other road users in busy intersections.

Evaluation of crop model-based simplified marginal net return maximising nitrogen application rates on site-specific level in maize

AbstractCrop growth models such as DSSAT-CERES-Maize have proven to be useful for analysing plant growth and yield within homogenous land units. The paper presents results of newly developed model-based site-specific Soil Profile Optimisation (SPO) tools in combination with an updated version of an already published Nitrogen Prescription Model (NPM). Site-specific soil profiles were generated through an inverse modelling approach based on measured site-specific yield (point-based) and tops weight (above-ground biomass time-series) and evaluated. Site-specific soil profiles generated based only on measured yield variability were able to explain 72% (R2 0.72) of yield variability (dependent variable) based on selected soil profile input parameters (independent variable). Site-specific soil profiles generated based on measured yield and tops variability simultaneously (multiple target variable) explained 68% of yield variability (R2 0.68). The NPM uses the SPO generated site-specific soil profiles for economic evaluation of site-specific N application rates. NPM simulated N application rates, aiming at the maximisation of marginal net return (MNR) were 25% lower compared to the uniform N application rates with an assumed grain and N price of 0.17 and 0.3 Euro kg−1 respectively, under rainfed conditions over three years based on soil profiles generated via an inverse modelling approach only from measured yield variability (one target variable). N application rates were 28% lower when based on soil profiles generated from simultaneously included grain and tops variability in the inverse modelling approach. The results highlight the importance of site-specific fertilizer management when maximising MNR.

Experimental testing combined with inverse-FE for mechanical characterisation of penile tissues

Erectile dysfunction (ED) predominantly affects men in their 40–70s and can lead to poor quality of life. One option for ED treatment is surgical implantation of an inflatable penile prosthesis (IPP). However, they can be associated with negative outcomes including infection, migration or fibrosis. To improve outcomes, the interaction between the IPP device and surrounding tissues needs further investigation and this could be achieved using pre-clinical testbeds, but they need to be informed by extensive tissue testing.In this study, an experimental approach is adopted to characterise the mechanics of horse penile tissue and establish a testing protocol for penile tissue. The whole penis segments were tested in plate compression tests to obtain whole penis behaviour which is necessary for validation of a pre-clinical testbed, whilst tensile and compression tests were performed on individual penile tissues, namely corpus cavernosa and tunica albuginea. The second part of the paper deals with the development of a computational model employing an inverse finite element approach to estimate the material parameters of each tissue layer. These material parameters are in good agreement with the experimental results obtained from the individual tissue layers and whole organ tissue tests. This paper presents the first study proposing realistic nonlinear elastic material parameters for penile tissues and offers a validated testbed for IPPs. Statement of significanceErectile Dysfunction (ED) affects over half the male population aged 40–70 potentially leading to poor quality of life. Patients not responding to conventional treatments of ED, are advised to use penile prostheses which can create an erection using implanted inflatable cylinders. A significant drawback of such prostheses, however, is the substantial tissue damage they can induce during their usage. Preclinical testbeds, including computational and bench-top models, could offer an efficient means of improving device designs to mitigate this damage but such testbeds require extensive knowledge of penile tissue properties. In this study, the authors determine penile tissue mechanics and apply an inverse FE approach to characterise the penile material properties required to validate preclinical models of the penis.

Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture

Despite the key role of irrigation in the Earth system, we lack fundamental information regarding the distribution of irrigated fields, irrigation timing and the amount of water utilized. In the past years, the SM_Delta and SM_Inversion approaches have been independently developed to provide estimates of irrigation timing and water amounts based on satellite soil moisture data. The SM_Delta approach retrieves irrigation from variations in soil moisture between an individual pixel and the surrounding rainfed area, while the SM_Inversion approach estimates the total amount of water entering the soil, then irrigation is derived by subtracting precipitation. In this study, we perform a comprehensive assessment of irrigation estimates from the SM_Delta and SM_Inversion algorithms based on Sentinel-1 surface soil moisture retrievals at 1 km resolution. Our analysis focuses on the Ebro basin, an irrigated region in Spain covering 83000 km2, during the period 2017–2019. We assess the ability of the two methods to discriminate irrigated and rainfed pixels, then we quantify the agreement of irrigation timing and water volumes with reference irrigation data. An inter-comparison between estimates from the SM_Delta and SM_Inversion methods is carried out considering both temporal and spatial features, i.e., monthly irrigation peaks and spatial irrigation patterns. Finally, we explore two potential applications of satellite-derived irrigation estimates: attributing irrigation water volumes to specific irrigation systems and to individual crops. We observe that both methods erroneously retrieve irrigation over rainfed pixels, and are therefore not suitable to map irrigated and rainfed fields. However, when auxiliary information on irrigated fields is available, we find a satisfactory agreement between district-scale reference data and satellite-retrieved irrigation, using both the SM_Delta and SM_Inversion approaches (Pearson R equal to 0.67 and 0.71, bias equal to −4.99 and −4.75 mm/15 days, respectively). When aggregated in space or time, the irrigation estimates exhibit coherent temporal dynamics and spatial patterns. For instance, estimates from both SM_Delta and SM_Inversion capture the delayed irrigation that occurred in 2018 due to wetter than usual conditions in spring. However, at the pixel-scale, limited consistency exists between irrigation estimates from the two methods due to different assumptions and parameterizations, e.g., use of constant vs pixel-specific soil water capacity (in the SM_Delta and SM_Inversion, respectively). Overall, the study demonstrates the reliability of irrigation estimates derived from the SM_Delta and SM_Inversion approaches, especially when shifting from small spatial and short temporal scales (pixel level, sub-weekly) to larger and longer scales (district level, seasonal). Hence, satellite-based irrigation estimates could inform water resources managers and basin authorities, as well as serve the modelling community by providing reliable information on the timing and the amounts of water employed at the basin level.

Global sensitivity of EEG source analysis to tissue conductivity uncertainties.

To reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial-but often neglected-aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface. We employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions. For sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity. Our results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.

Poroelastic Characterization and Modeling of Subcutaneous Tissue Under Confined Compression.

Subcutaneous tissue mechanics are important for drug delivery. Yet, even though this material is poroelastic, its mechanical characterization has focused on its hyperelastic response. Moreover, advancement in subcutaneous drug delivery requires effective tissue mimics such as hydrogels for which similar gaps of poroelastic data exist. Porcine subcutaneous samples and gelatin hydrogels were tested under confined compression at physiological conditions and strain rates of 0.01%/s in 5% strain steps with 2600s of stress relaxation between loading steps. Force-time data were used in an inverse finite element approach to obtain material parameters. Tissues and gels were modeled as porous neo-Hookean materials with properties specified via shear modulus, effective solid volume fraction, initial hydraulic permeability, permeability exponent, and normalized viscous relaxation moduli. The constitutive model was implemented into an isogeometric analysis (IGA) framework to study subcutaneous injection. Subcutaneous tissue exhibited an initial spike in stress due to compression of the solid and fluid pressure buildup, with rapid relaxation explained by fluid drainage, and longer time-scale relaxation explained by viscous dissipation. The inferred parameters aligned with the ranges reported in the literature. Hydraulic permeability, the most important parameter for drug delivery, was in the range mm /(N s). With these parameters, IGA simulations showed peak stresses due to a 1-mL injection to reach 48.8 kPa at the site of injection, decaying after drug volume disperses into the tissue. The poro-hyper-viscoelastic neo-Hookean model captures the confined compression response of subcutaneous tissue and gelatin hydrogels. IGA implementation enables predictive simulations of drug delivery.

On mathematical problems of two-coefficient inverse problems of ultrasonic tomography

This paper proves the theorem of uniqueness for the solution of a coefficient inverse problem for the wave equation in (with two unknown coefficients: speed of sound and absorption. The original nonlinear coefficient inverse problem is reduced to an equivalent system of two uniquely solvable linear integral equations of the first kind with respect to the sound speed and absorption coefficients. Estimates are made, substantiating the multistage method for two unknown coefficients. These estimates show that given sufficiently low frequencies and small inhomogeneities, the residual functional for the nonlinear inverse problem approaches a convex one. This solution method for nonlinear coefficient inverse problems is not linked to the limit approach as frequency tends to zero, but assumes solving the inverse problem using sufficiently low, but not zero, frequencies at the first stage. For small inhomogeneities that are typical, for instance, for medical tasks, carrying out real experiments at such frequencies does not present major difficulties. The capabilities of the method are demonstrated on a model inverse problem with unknown sound speed and absorption coefficients. The method effectively solves the nonlinear problem with parameter values typical for tomographic diagnostics of soft tissues in medicine. A resolution of approximately 2 mm was achieved using an average sounding pulse wavelength of 5 mm.

Tunnel resistivity deep learning inversion method based on physics‐driven and signal interpretability

AbstractData‐driven deep learning technology has a strong non‐linear mapping ability and has good development potential in geophysical inversion problems. Traditional inversion techniques offer broad generality, but they can remain trapped in local minima, particularly for three‐dimensional tunnelling resistivity inversion. In this work, we present an inversion methodology that combines traditional physics‐driven and deep learning data‐driven inversion approaches. To further support deep neural networks' dependability on unseen data, the interpretability of their working mechanism is explored. We execute migration learning based on small sample data after identifying the critical parameters that restrict the effectiveness of inversion by analysing the feature maps of various model data. We demonstrate, using both synthetic examples and field data, that the proposed method can improve the accuracy in detecting water‐bearing anomalies (caves and faults), which are typically encountered during tunnel excavation.

Deep learning applied to CO2 power plant emissions quantification using simulated satellite images

Abstract. The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo-XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2, NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images.

An in-silico planning study of stereotactic body radiation therapy for polymetastatic patients with more than ten extra-cranial lesions

Background and purposeLimited data is available about the feasibility of stereotactic body radiation therapy (SBRT) for treating more than five extra-cranial metastases, and almost no data for treating more than ten. The aim of this study was to investigate the feasibility of SBRT in this polymetatstatic setting. Materials and methodsConsecutive metastatic melanoma patients with more than ten extra-cranial metastases and a maximum lesion diameter below 11 cm were selected from a single-center prospective registry for this in-silico planning study. For each patient, SBRT plans were generated to treat all metastases with a prescribed dose of 5x7Gy, and dose-limiting organs (OARs) were analyzed. A cell-kill based inverse planning approach was used to automatically determine the maximum deliverable dose to each lesion individually, while respecting all OARs constraints. ResultsA total of 23 polymetastatic patients with a medium of 17 metastases (range, 11–51) per patient were selected. SBRT plans with sufficient target coverage and respected OARs dose constraints were achieved in 16 out of 23 patients. In the remaining seven patients, the lungs V5Gy < 80 % and the liver D700 cm3 < 15Gy were most frequently the dose-limiting constraints. The cell-kill based planning approach allowed optimizing the dose administration depending on metastases total volume and location. ConclusionThis retrospective planning study shows the feasibility of definitive SBRT for 70% of polymetastatic patients with more than ten extra-cranial lesions and proposes the cell-killing planning approach as an approach to individualize treatment planning in polymetastatic patients’.

A temporal gap filling approach for GNSS-IR soil moisture inversion based on generalized extension

Soil moisture monitoring is a research hotspot in the application of navigation satellite reflected signals. In order to retrieve soil moisture in any period with high precision and solve the problem that soil moisture cannot be retrieved when satellite observation data is partially missing, the principle of generalized extension in engineering science is applied to GNSS-IR technology in the paper. Firstly, the generalized extension approximation method is used to interpolate the precise ephemeris to obtain the altitude angle information of each satellite in each epoch. Then, the generalized extendedinterpolation and extrapolation models were used to supplement the initial phase of the reflected signal corresponding to the missing data as the input of the inversion model to predict soil moisture. The soil moisture inversion experiment was completed according to the observation data provided by PBO. The results show that the generalized extension method can cope with the missing data of different lengths and distributions in GNSS-IR soil moisture retrieval, When the data were confirmed for 15 days, the correlation coefficient of inversion results was 0.916, RSME was 0.03358 cm3/cm3, and MAE was 0.02912 cm3/cm3, and the correlation coefficient of more than 0.85 can be maintained when the amount of missing data reaches 60 % of the test set.

2D boundary-condition-free nonlinear inversion technique applied to optical shear vibration induced microelastography

Optical microelastography (OME) has emerged as a new technique for quantifying cellular mechanical properties. However, accurately reconstructing viscoelastic properties at the microscale level from noisy 2D displacement fields remains a challenge. This study introduces a 2D boundary-condition-free nonlinear inversion (2D-NoBC-NLI) approach, addressing challenges of interpreting noisy data and deducing full-field 3D displacements from 2D measurements. OME requires vibrating the cell and mapping the shear modulus based on wave-induced displacements within the cell. The shear modulus distribution is recovered via a coupled adjoint field NLI reconstruction to allow 2D-NoBC-NLI. Validation was conducted through numerical simulations at 36 kHz on a homogeneous sphere of 75 μm diameter and an assigned viscoelastic modulus, G*, of 800 + i150 Pa. The same reconstruction approach was also applied to experimental data obtained from polyacrylamide (PAAm) microbeads of the same diameter. Results demonstrated relative differences from true simulated values of 0.7% and 45% for storage and loss moduli, respectively, with a coefficient of variation under 1% for homogeneous regions. When applying this method to PAAm microbeads, viscoelastic reconstructions showed the potential of OME under experimental conditions. These findings highlight the accuracy of 2D-No BC-NLI reconstruction in OME for precise microscale characterization and mapping of the viscoelastic cell structure.

Developing deep learning methods for pre-stack seismic data inversion

Inversion of pre-stack seismic data is important for building accurate models of hydrocarbon reservoirs used to estimate reserves and set up efficient production strategies. Conventional methods often involve challenging procedures. The limited bandwidth, noisy nature, and fact that the pre-stack seismic data has more than one component all affect how accurate and stable the inversion solution is. Deep learning (DL) is a cost-effective and accurate method for predicting spatially distributed properties. We utilized specific deep-learning algorithms to create a methodology for estimating porosity based on angle gathers. The workflow involved trace editing for amplitude variation compensation, noise reduction, and organizing data into appropriate common depth point (CDP) gathers. Well-logging data and approved seismic horizons were utilized to obtain the corresponding CDP gather’ porosity labels. From initial CDP gathers, we computed a series of seismic attributes and developed an efficient technique to identify the most relevant ones that we utilized to train base models. We explored different hyper-parameters to determine the optimal characteristics for the method's objective function. We subsequently developed an integration approach that assigns appropriate weights to aggregate individual base models to create a robust model. We developed a statistical analysis method to determine the confidence level in the final prediction. In real-world scenarios, the new methodology outperformed conventional and popular DL inversion approaches, with an R2 of 0.989 compared to 0.954 and 0.967, a mean porosity of 0.174868 compared to 0.174985, and a mean uncertainty of ±0.000714 at the well location.

Characterizing Offshore Freshened Groundwater Salinity Patterns Using Trans‐Dimensional Bayesian Inversion of Controlled Source Electromagnetic Data: A Case Study From the Canterbury Bight, New Zealand

AbstractThe study of offshore freshened groundwater (OFG) is gaining importance due to population growth and environmental pressure on coastal water resources. Marine controlled source electromagnetic (CSEM) methods can effectively map the spatial extent of OFG systems using electrical resistivity as a proxy. Integrating these resistivity models with sub‐surface properties, such as host‐rock porosity, allows for estimates of pore‐water salinity. However, evaluating the uncertainty in pore‐water salinity using resistivity models obtained from deterministic inversion approaches presents challenges, as they provide only one best‐fit model, with no associated estimate of uncertainty. To address this limitation, we employ trans‐dimensional Markov‐Chain Monte‐Carlo inversion on marine time‐domain CSEM data, acquired in the Canterbury Bight, New Zealand. We integrate resistivity posterior probability distributions with borehole and seismic reflection data to estimate pore‐water salinity with corresponding uncertainty estimates. The results highlight a low‐salinity groundwater body in the center of the survey area, hosted by consecutive silty‐ and fine‐sand layers approximately 20–60 km from the coast. The posterior probability distribution of resistivity models indicates freshening of the OFG body toward the shoreline within a permeable, coarse‐sand layer 40–150 m beneath the seafloor, suggesting an active connection between the OFG body and the terrestrial groundwater system. The approach demonstrates how Bayesian inversion constrains the uncertainties in resistivity models and subsequently in pore‐water salinity estimates. Our findings highlight the potential of Bayesian inversion to enhance our understanding of OFG systems and provide uncertainty constraints for hydrogeological modeling, thereby contributing to sustainable water resource development.

Uncovering drone intentions using control physics informed machine learning

Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important to assigning risk and executing countermeasures. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes using observational data alone is inherently unreliable due to observational and learning bias. Here, we developed a control-physics informed machine learning (CPhy-ML) that can robustly infer across intention classes. The CPhy-ML couples the representation power of deep learning with the conservation laws of aerospace models to reduce bias and instability. The CPhy-ML achieves a 48.28% performance improvement over traditional trajectory prediction methods. The reward inference results outperforms conventional inverse reinforcement learning approaches, decreasing the root mean squared spectral norm error from 3.3747 to 0.3229.

Global change alters coastal plankton food webs by promoting the microbial loop: An inverse modelling and network analysis approach on a mesocosm experiment

Marine organisms are currently, and will continue to be, exposed to the simultaneous effects of multiple environmental changes. Plankton organisms form the base of pelagic marine food webs and are particularly sensitive to ecosystem changes. Thus, warming, acidification, and changes in dissolved nutrient concentrations have the potential to alter these assemblages, with consequences for the entire ecosystem. Despite the growing number of studies addressing the potential influence of multiple drivers on plankton, global change may also cause less obvious alterations to the networks of interactions among species. Using inverse analyses applied to data collected during a mesocosm experiment, we aimed to compare the ecological functioning of coastal plankton assemblages and the interactions within their food web under different global change scenarios. The experimental treatments were based on the RCP 6.0 and 8.5 scenarios developed by the IPCC, which were extended (ERCP) to integrate the future predicted changes in coastal water nutrient concentrations. Overall, we identified that the functioning of the plankton food web was rather similar in the Ambient and ERCP 6.0 scenarios, but substantially altered in the ERCP 8.5 scenario. Using food web modelling and ecological network analysis, we identified that global change strengthens the microbial loop, with a decrease of energy transfer efficiency to higher trophic levels. Microzooplankton responded as well by an increased degree of herbivory in their diet and represented, compared to mesozooplankton, by far the main top-down pressure on primary producers. We also observed that the organisation of the food web and its capacity to recycle carbon was higher under the ERCP 8.5 scenario, but flow diversity and carbon path length were significantly reduced, illustrating an increased food web stability at the expense of diversity. Here, we provide evidence that if global change goes beyond the ERCP 6.0 scenario, coastal ecosystem functioning will be subjected to dramatic changes.

Identification of creep damage properties of coating via a compound miniaturized testing method and an inverse approach

A systematic analytical approach has been developed to comprehensively characterize the creep damage material properties of coatings on thin-layer substrates. This involves conducting creep tests using a compound, two-material, miniaturized thin plate specimen (MTPS) with an inverse approach. For demonstration, independent creep tests were performed on miniature specimens of the substrate (P91 steel) and the coating (CoNiCrAlY) at 650 °C, as well as on compound substrate-coating specimens. The reference stress method, coupled with finite element analysis, was utilized to obtain conversion parameters for strain data from the miniature specimen tests. Analytical solutions for the Kachanov creep damage model specific to the two-material substrate-coating system were adopted for inverse optimization. The MTPS testing method and the associated inverse approach have proven effective in accurately determining the complete three-stage creep constants within the coating-substrate system. Results obtained for the P91–CoNiCrAlY coated system have shown a clear demonstration of the accuracy and capability of the method in identifying the full sets of uniaxial creep damage properties, i.e. A, n, m, B, χ, and φ, provided the feasible experimental data are available.

Optimizing Polarization Selective Unidirectional Photoluminescence from Phased‐Array Metasurfaces

AbstractMetasurface‐based optical elements offer a wide design space for miniature and lightweight optical applications. Typically, metasurface optical elements transform an incident light beam into a desired output waveform. Recent demonstrations of light‐emitting metasurfaces highlight the potential for directly producing desired output waveforms via metasurface‐mediated spontaneous emission. In this work, reciprocal finite‐difference time‐domain (FDTD) simulations and machine learning are used to enable the inverse design of highly unidirectional photoluminescent III‐Nitride quantum well metasurfaces capable of directive p‐, s‐, or combined p‐ and s‐ polarized emission at arbitrary angles. In comparison with previous intuition‐guided designs using the same quantum well architectures, the inverse design approach enables new polarization capabilities and experimentally demonstrated improvements in directivity of 54%. An analysis of ways in which the inverse design both validates and contradicts previous intuition‐guided design heuristics is presented. Ultimately, the combination of reciprocal simulations and efficient global optimization (EGO) grants remarkable improvements in emission directivity and results in full control over the polarization and momentum of emitted light, including simultaneous directional emission of s‐ and p‐polarized light.

Theoretical study of the synergic relationships between the design parameters in energy-saving building design

With the rapid development of the economy, people have increasingly higher requirements for the comfort of living spaces, and the result is the sharp increase in building energy consumption. Several design parameters influence living space comfort and building energy efficiency. Since the same design standard can include different design parameter combinations, synergic relationships may exist between these criteria for one case. Identifying these synergic relationships requires an inverse problem approach. This paper established a model by combining an improved genetic algorithm (IGA) and numerical calculation to determine the synergic design parameter relationships (e.g. the thermophysical building material properties and energy-saving factors). For Isum=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{I}}}_{{\ ext{sum}}}= {0} $$\\end{document}, the shading coefficient significantly influenced the linear function between the thermal conductivity and insulation thickness. In this case, the insulation thickness was exponentially related to the shading coefficient, while the thermal conductivity of the insulation material significantly impacted the synergic relationship. For ESR=65%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ESR}}{=65}\\%$$\\end{document}, the insulation thickness was a segmented function of the shading coefficient. The results verified that the proposed model was efficient and reliable for identifying the synergic relationships between energy-saving parameters. In engineering applications, designers can select the optimal design parameter combination based on the relationship curve between the parameters in this paper according to the local market conditions and specific design requirements.

A unified approach to inverse robust optimization problems

A variety of approaches has been developed to deal with uncertain optimization problems. Often, they start with a given set of uncertainties and then try to minimize the influence of these uncertainties. The reverse view is to first set a budget for the price one is willing to pay and then find the most robust solution. In this article, we aim to unify these inverse approaches to robustness. We provide a general problem definition and a proof of the existence of its solution. We study properties of this solution such as closedness, convexity, and boundedness. We also provide a comparison with existing robustness concepts such as the stability radius, the resilience radius, and the robust feasibility radius. We show that the general definition unifies these approaches. We conclude with an example that demonstrates the flexibility of the introduced concept.