Inverse Optimization Research Articles

Leveraging the T2T assembly to resolve rare and pathogenic inversions in reference genome gaps.

Chromosomal inversions (INVs) are particularly challenging to detect due to their copy-number neutral state and association with repetitive regions. Inversions represent about 1/20 of all balanced structural chromosome aberrations and can lead to disease by gene disruption or altering regulatory regions of dosage-sensitive genes in cis Short-read genome sequencing (srGS) can only resolve ∼70% of cytogenetically visible inversions referred to clinical diagnostic laboratories, likely due to breakpoints in repetitive regions. Here, we study 12 inversions by long-read genome sequencing (lrGS) (n = 9) or srGS (n = 3) and resolve nine of them. In four cases, the inversion breakpoint region was missing from at least one of the human reference genomes (GRCh37, GRCh38, T2T-CHM13) and a reference agnostic analysis was needed. One of these cases, an INV9 mappable only in de novo assembled lrGS data using T2T-CHM13 disrupts EHMT1 consistent with a Mendelian diagnosis (Kleefstra syndrome 1; MIM#610253). Next, by pairwise comparison between T2T-CHM13, GRCh37, and GRCh38, as well as the chimpanzee and bonobo, we show that hundreds of megabases of sequence are missing from at least one human reference, highlighting that primate genomes contribute to genomic diversity. Aligning population genomic data to these regions indicated that these regions are variable between individuals. Our analysis emphasizes that T2T-CHM13 is necessary to maximize the value of lrGS for optimal inversion detection in clinical diagnostics. These results highlight the importance of leveraging diverse and comprehensive reference genomes to resolve unsolved molecular cases in rare diseases.

UAV-Based Multispectral Winter Wheat Growth Monitoring with Adaptive Weight Allocation

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R2) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.

Acoustic structure inverse design and optimization using deep learning

From ancient to modern times, acoustic structures have been employed to manage the spread of acoustic waves. Nevertheless, designing these structures traditionally remains a laborious and computationally intensive iterative process. Recognizing that complex acoustic systems can be effectively analyzed using the lumped-parameter method, we introduce a deep learning model that learns the correlation between the equivalent electrical parameters and the acoustic properties of these structures. As an illustration, we consider the design of multi-order Helmholtz resonators, showing experimentally that our model can predict structures with high precision that closely align with the specified design criteria. Furthermore, our model can seek multiple solutions in conjunction with dimensionality reduction algorithms and support evolutionary algorithms in optimization tasks. Compared to traditional numerical methods, our approach offers greater efficiency, flexibility, and universality. The designed acoustic structures hold broad potential for applications including speech enhancement, sound absorption, and insulation.

High strain-rate fracture behavior of a hollow projectile

In this paper, a hollow projectile made of AISI4340 steel was fabricated based on an arbitrary warhead shape, and high-speed impact tests were performed according to various target encounter conditions including velocity and angle. In order to characterize the dynamic fracture behavior, tensile tests were performed on various notch specimens under three strain rates (10–3 s-1, 100 s-1, 103 s-1), and the stress state histories of the specimens were calibrated through a hybrid experimental-numerical analysis. In particular, for the intermediate (100 s-1) and high (103 s-1) strain rate conditions, the local temperature rose due to adiabatic heating until fracture was experimentally measured, and the thermal softening behavior determined from the inverse optimization technique was considered for the dynamic constitutive model. For the comparison with the high-speed impact test results, a rate-dependent ductile fracture model was utilized for numerical simulation. Considering that the model parameters were calibrated with the thermal softening effect, the proposed fracture model implicitly takes temperature into account. The deformation and fracture modes of the projectile from experimental and numerical study showed very good agreement under all impact conditions. It was confirmed that the softening of a material by adiabatic heating should be considered along with strain rate hardening in dynamic fracture simulation.

A Deep Learning Inversion Method for Airborne Time-Domain Electromagnetic Data Using Convolutional Neural Network

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods.

Data-driven inverse design of a multiband second-order phononic topological insulator

Second-order phononic topological insulators (SPTIs) have sparked vast interest in manipulating elastic waves, owing to their unique topological corner states with robustness against geometric perturbations. However, it remains a challenge to develop multiband SPTIs that yield multi-frequency corner states using prevailing forward design approaches via trial and error, and most inverse design approaches substantially rely on time-consuming numerical solvers to evaluate band structures of phononic crystals (PnCs), showing low efficiency particularly when applied to different optimization tasks. In this study, we develop and validate a new inverse design framework, to enable the multiband SPTI by integrating data-driven machine learning (ML) with genetic algorithm (GA). The relationship between shapes of scatterers and frequency bounds of multi-order bandgaps of PnCs is mapped via developing artificial neural networks (ANNs), and a multiband SPTI with multi-frequency topological corner states is cost-effectively designed using the proposed inverse optimization framework. Our results indicate that the data-driven approach can provide a high-efficiency solution for on-demand inverse designs of multiband second-order topological mechanical devices, enabling diverse application prospects including multi-frequency robust amplification and confinement of elastic waves.

Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data

The extensive accumulation of tailings can potentially cause heavy metal contamination in the surrounding farmland soil. Accurately predicting the spatial distribution of heavy metals in farmland soil is crucial for assessing the potential environmental hazards of tailings.This study focuses on the spatial distribution and the quantitative prediction of heavy metals (chromium (Cr), vanadium (V), and copper (Cu)) in soils surrounding mine tailings using advanced spectral data analysis and multiple prediction models. The original hyperspectral reflectance data were processed using first-order differential (FD), second-order differential (SD), reciprocal logarithmic (LR), and continuum removal (CR) transformations to highlight the positions of characteristic bands. Multiple linear regression (MLR), stepwise linear regression (SLR), partial least squares regression (PLSR), random forest (RF), and back propagation artificial neural network (BP-ANN) models were used to establish inversion models for Cr, V, and Cu based on bands with high correlation coefficients. The performance of the inversion models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). The results indicate that the raw hyperspectral data from the measured soil exhibit a weak response to heavy metal content in the study area. However, applying FD, SD, and CR transformations significantly enhances the sensitivity of soil spectral data to heavy metal concentrations, facilitating subsequent modeling. Among these, the SD transformation is particularly beneficial for modeling the Cr and Cu elements in the soil. For the V element, the FD transformation yields data that are more suitable for modeling. Regarding the inversion models based on the measured spectral data, the BP-ANN model exhibited the best predictive performance. Specifically, when combined with SD spectral data, the BP-ANN achieved the highest predictive accuracy for Cu content (R² = 0.85, RPD = 2.12). The RF model demonstrated the next best performance, with its optimal inversion model also utilizing SD spectral data for predicting Cu content (R² = 0.76, RPD = 1.90). On the other hand, the MLR model exhibited the poorest performance and is unsuitable for predicting heavy metal content in the region using the measured spectral data. This study highlights the potential of spectral data in environmental monitoring and provides a technical reference for the inversion assessment and regulation of heavy metals in farmlands surrounding tailing sites.

Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms

Enhancing the Efficiency and Accuracy of Inverse Optimization Inverse optimization (IO) is used to model the behavior of decision-making agents who solve optimization problems in response to external signals. Inspired by the geometry of IO problems, in “Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms,” Zattoni Scroccaro, Atasoy, and Mohajerin Esfahani propose the “incenter” concept to solve IO problems, which unlike previously proposed approaches, can be used to derive computationally tractable solutions to this modeling problem. Moreover, they also propose a novel loss function for IO problems and a tailored optimization algorithm to optimize it. Extensive numerical experiments showcase the improved efficiency and accuracy of the proposed IO formulations and algorithm.

Software and Date for "Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms"

Software and Date for "Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms"

Application of Experimental Configurations of Seismic and Electric Tomographic Techniques to the Investigation of Complex Geological Structures

The main purpose of this study is the subsurface investigation of two complex geological environments focusing on the improvement of data acquisition and processing parameters regarding electric and seismic tomographic techniques. Two different study areas, in central–east Peloponnese and SE Attica, were selected, where detailed geological mapping and surface geophysical survey were carried out. The applied geophysical survey included the application of electrical resistivity tomography (ERT), seismic refraction tomography (SRT) and ground penetrating radar (GPR). The geoelectrical measurements were acquired with different arrays and electrode configurations. Moreover, various types of seismic sources were used at seventeen shot locations along the seismic arrays. For the processing of geoelectrical data, clustered datasets were created, increasing the depth of investigation and discriminatory capability. The seismic data processing included the following: (a) the creation of synthetic models and seismic records to determine the effectiveness and capabilities of the technique, (b) spectral analysis of the seismic records to determine the optimal seismic source type and (c) inversion of the field data to create representative subsurface velocity models. The results of the two techniques successfully delineated the complex subsurface structure that characterizes these two geological environments. The application of the ERT combined with the SRT are the two dominant, high-resolution techniques for the elucidation of complex subsurface structures.

Hydrogen susceptibility of Al 5083 under ultra-high strain rate ballistic loading

Abstract The effect of hydrogen on the ballistic performance of aluminum (Al) 5083H131 was examined both experimentally and numerically in this study. Ballistics tests were conducted at a 30° obliquity in accordance with the ballistic test standard MIL-DTL-46027 K. The strike velocities of projectiles were ranged from 240 m s−1 to 500 m s−1 level in the room temperature. Electrochemical hydrogen charging method was utilized to introduce hydrogen into material. Chemical composition of material was analyzed using energy dispersive X-ray (EDX) analysis. Instant camera pictures were captured using high-speed camera to compare H-uncharged and H-charged specimen ballistics tests. The volume loss in partially penetrated specimens were assessed using the 3D laser scanning method. Microstructural examinations were conducted utilizing scanning electron microscopy (SEM). It was observed that with the increased deformation rate, the dominance of the HEDE mechanism over HELP became evident. Furthermore, the experimental findings were corroborated through numerical methods employing finite element analysis (FEM) along with the Johnson–Cook plasticity model and failure criteria. Inverse optimization technique was employed to implement and fine-tune the Johnson–Cook parameters for H-charged conditions. Upon comparing the experimental and numerical outcomes, a high degree of consistency was observed, indicating the effective performance of the model.

Reconfigurable ultrasound focusing effect through acoustic barriers

The low transmission efficiency of ultrasonic waves in waveguides of a high acoustic impedance (referred to as dense materials), due to the impedance mismatch between the background media and the dense materials, poses a significant obstacle to practical applications of high-intensity focused ultrasound (HIFU) such as ultrasound therapy or medical imaging. To address this challenge, we present an inverse optimization scheme for fabrication of novel acoustic meta-lenses, enabling strengthened penetration and enhanced focusing of ultrasonic waves when the waves traverse barriers. Both simulation and experiment validate the effectiveness of the developed meta-lenses which are annexed to hemispherical plates, and demonstrate an enhanced transmission of the sound power by an order of magnitude compared to a scenario without the use of the meta-lens. The focal distance is reconfigurable by adjusting the geometric parameters of the meta-lenses. The proposed design philosophy is not restricted by the complexity of the target structures, and it allows the ultrasonic waves to pass through acoustic barriers with a non-uniform thickness yet maintaining efficient wave focusing. This study holds appealing applications in HIFU-enabled ultrasound imaging and therapy.

Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China

To investigate effective techniques for estimating rice production in hilly and mountainous areas, in this study, we collected yield data at the field level, agro-meteorological data, and Sentinel-2/MSI remote sensing data in Chongqing, China, between 2020 and 2023. The integral values of vegetation indicators from the rice greening up to heading–filling stages were determined using the Newton–trapezoidal integration method. Using correlation analysis and importance analysis of permutation features, the effects of agro-meteorological variables and vegetation index integrals on rice yield were assessed. The chosen characteristics were then combined with three machine learning techniques—random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR)—to create six rice yield estimate models. The results showed that combined vegetation indices were more effective than indices used in separate development phases. Specifically, the correlation coefficients between the integral values of eight vegetation indices from rice greening up to heading–filling stages and rice yield were all above 0.65. By introducing agro-meteorological factors as new independent variables and combining them with vegetation indices as input parameters, the predictive capability of the model was evaluated. The results showed that the performance of PLSR remained stable, while the prediction accuracies of SVM and RF improved by 13% to 21.5%. After feature selection, the inversion performance of all three machine learning models improved, with the RF model coupled with variables selected during permutation feature importance analysis achieving the optimal inversion effect, which was characterized by a coefficient of determination of 0.85, a root mean square error of 529.1 kg/hm2, and a mean relative error of 5.63%. This study provides technical support for improving the accuracy of remote sensing-based crop yield estimation in hilly and mountainous regions, facilitating precise agricultural management and informing agrarian decision making.

Adaptive neural inverse optimal control for uncertain switched nonlinear systems: an improved predefined-time method

For the first time, this paper introduces an improved predefined-time inverse optimal method to address the adaptive tracking control problem of a class of uncertain switched nonlinear systems. The proposed approach enables the system to achieve optimal performance without resorting to the HJB equation. Firstly, we design two-time constants to improve the traditional predefined-time lemma. The improved predefined-time lemma exhibits faster convergence. Subsequently, to realise the design of a predefined-time inverse optimal controller, a specific form of the auxiliary controller is designed using Sontag functions and nonsingular functions. Furthermore, the stability of the system and the optimality of the inverse are proven through the use of a common Lyapunov function method, ensuring that tracking errors converge to the neighbourhood of zero within the predefined time. Finally, the rationality of the proposed control structure is demonstrated through the example results.

One-step fabrication of hetero-structured polyethersulfone hollow fiber membranes through surface segregation for pervaporation desalination

Hollow fiber membranes (HFMs) are an ideal configuration for industrial applications of pervaporation desalination (PV) membranes. Currently, the efficient fabrication of PV HFMs is restricted by multi-step post-treatment processes on their outer surfaces to form the selective layer. Herein, we utilized the surface segregation strategy to fabricate a hetero-structured HFM in one step, simultaneously forming the hydrophilic dense outer layer and the porous support layer. During hollow fiber spinning process, the amphiphilic copolymer polyvinylpyrrolidone-polyvinyl acetate (PVP-PVAc) in polyether sulfone (PES) dope solution migrated towards water contacting interface in the coagulation bath and formed the hydrophilic layer by extending hydrophilic segment outwards. This fast in-situ hydrophilization well matched the spinning efficiency. The optimal PVP-PVAc content and phase inversion temperature were investigated and the spinning parameters on the structure and performance of HFMs were studied. The HFMs prepared realized a flux of 26.70 kg m−2 h−1 and the salt rejection of 99.98 %, along with adaptability to solutions with different salinities and excellent anti-organic fouling property. This study may provide a facile method to fabricate HFMs with desirable PV desalination performance and scaling-up potential.

Monitoring aboveground organs biomass of wheat and maize: A novel model combining ensemble learning and allometric theory

Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGBLeaf), stem (AGBStem), and reproductive organs (AGBR) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGBLeaf inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGBLeaf inversion hybrid model was coupled with the allometric model to estimate the AGBStem and AGBR in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn’t improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGBLeaf datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R2) of wheat and maize AGBStem were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R2 of AGBR was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model’s transferability is necessary to increase the potential for generalization in production practice.

Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies

Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies

BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification

Data-driven inverse optimization (IO) aims to estimate unknown parameters in an optimization model from observed decisions. The IO problem is commonly formulated as a large-scale bilevel program that is notoriously difficult to solve. We propose a derivative-free optimization approach based on Bayesian optimization, BO4IO, to solve general IO problems. The main advantages of BO4IO are two-fold: (i) it circumvents the need of complex reformulations or specialized algorithms and can hence enable computational tractability even when the underlying optimization problem is nonconvex or involves discrete variables, and (ii) it allows approximations of the profile likelihood, which provide uncertainty quantification on the IO parameter estimates. Our extensive computational results demonstrate the efficacy and robustness of BO4IO to estimate unknown parameters from small and noisy datasets. In addition, the proposed profile likelihood analysis effectively provides good approximations of the confidence intervals on the parameter estimates and assesses the identifiability of the unknown parameters.

Flexibility Characterization of Sustainable Power Systems in Demand Space: A Data-Driven Inverse Optimization Approach

Flexibility Characterization of Sustainable Power Systems in Demand Space: A Data-Driven Inverse Optimization Approach

Theory of classical and modified spaceborne synthetic aperture radar imaging

This paper presents a novel method for spaceborne synthetic aperture radar (SAR) imaging, achieved through the statistical solution of the inverse optimization problem of image reconstruction, specifically focusing on radar cross-section (RCS). The proposed method improves upon traditional techniques by incorporating signal decorrelation, implemented via inverse filtering. A comparative analysis is provided between the modified method and the classical aperture synthesis approach, both of which are grounded in the optimization problem solution within the theory of the maximum likelihood method.