Surrogate Approach Research Articles

Doing More with Less on Intracranial Pressure Monitoring

Intracranial pressure (ICP) management based on predetermined thresholds is not accurate in light of recent research on cerebrovascular physiology. Interpersonal and intrapersonal variations will lead ICP elevations to reach individualized thresholds for intracranial compliance impairment from one subject to another. Therefore reuniting the modern techniques of neuromonitoring besides ICP enables practitioners to have a more whole picture in anticipating neuro worsening and improving timing in decision making. Brief literature review. For the severely brain-injured patient, current evidence indicates a personalized and physiology-based multimodal monitoring care to be required rather than decision making according to ICP predetermined cut-offs. The authors' point of view is of particular importance for regions with resource heterogeneity and scarcity, where ICP monitoring is not available for all those in need and noninvasive techniques may provide a surrogate approach. If physicians who deal with acute-brain-injured patients in lower-resource areas understand that several tools besides ICP may improve their practice, it is possible to reduce acute brain injury morbimortality.

Optimizing Quantum Control Pulses with Gaussian Process Priors: The Spectral Way.

This study presents the Gaussian Process Prior Optimization for Pulse Shaping (GPPOPS) methodology, a novel approach to pulse shaping engineering. Its main objective is to efficiently identify laser pulse shapes that can achieve a desired task encoded in a cost function while being experimentally implementable. The AlH+ molecule was utilized as a test case to find pulse shapes that maximized vibronic transitions. The results demonstrate that optimal pulses can be readily implemented using current laser technology and that their control capabilities can withstand noise. The study emphasizes the benefits of constructing a surrogate approach to the control landscape during the optimization process. This approach is expected to be versatile, efficient and readily implementable in the laboratory. Its demonstrated robustness to noise sets it apart from other numerical pulse shaping engineering methods. By reducing the required experimental labor, this method has the potential to facilitate breakthroughs in quantum engineering.

A journey from mechanistic to data-driven models in process engineering: dimensionality reduction, surrogate and hybrid approaches, and digital twins

A journey from mechanistic to data-driven models in process engineering: dimensionality reduction, surrogate and hybrid approaches, and digital twins

Utility and Clinical Application of Circulating Tumor DNA (ctDNA) in Advanced Prostate Cancer

The treatment landscape for metastatic prostate cancer has undergone significant changes in recent years. The availability of next-generation imaging techniques and the emergence of novel therapies have led to earlier and more aggressive treatment approaches for patients. However, despite these advancements, drug resistance and progression to castration-resistant disease remain inevitable. Understanding the molecular landscape of advanced prostate cancer lies at the forefront of being able to deliver personalized therapies and more robustly risk-stratify patients, when combined with clinical factors. Advanced prostate cancer is characterized by inter- and intratumoral heterogeneity, posing challenges in comprehensively analyzing the genomic tumor profile using a solitary tissue sample. Additionally, the disease often manifests as bone-predominant metastatic tumors, making biopsies impractical in many cases. Moreover, archival tissue samples from a prostatectomy specimen may not accurately represent the current state of the tumor. To overcome these limitations, liquid biopsies using plasma samples have emerged as a minimally invasive surrogate approach to obtain real-time information on the genomic tumor profile. Growing evidence confirms the excellent concordance of liquid biopsies with tissue samples, making them an attractive alternative to traditional tissue biopsies. These assays can provide predictive and prognostic information that may enhance patient discussions and influence treatment decisions. This review focuses on the evolution and utility of circulating tumor-derived DNA (ctDNA) liquid biopsy assays in metastatic prostate cancer.

A perspective on In vitro developmental neurotoxicity test assay results: An expert panel review

For decades, there has been increasing concern about the potential developmental neurotoxicity (DNT) associated with chemicals. Regulatory agencies have historically utilized standardized in vivo testing to evaluate DNT. Owing to considerations including higher-throughput screening for DNT, reduction in animal use, and potential cost efficiencies, the development of alternative new approach methods (NAMs) occurred; specifically, the advent of the DNT in vitro test battery (DNT IVB). SciPinion convened an expert panel to address specific questions related to the interpretation of in vitro DNT test data. The consensus of the expert panel was that the DNT IVB might be used during initial screening, but it is not presently a complete or surrogate approach to determine whether a chemical is a DNT in humans. By itself, the DNT IVB does not have the ability to capture nuances and complexity of the developing nervous system and associated outcomes including behavioral ontogeny, motor activity, sensory function, and learning/memory. Presently, such developmental landmarks cannot be adequately assessed in the DNT IVB or by other NAMs. The expert panel (all who serve as co-authors of this review) recommended that additional data generation and validation is required before the DNT IVB can be considered for application within global regulatory frameworks for decision-making.

Multi-fidelity machine learning based uncertainty quantification of progressive damage in composite laminates through optimal data fusion

Recently machine learning (ML) based approaches have gained significant attention in dealing with computationally intensive analyses such as uncertainty quantification of composite laminates. However, high-fidelity ML model construction is computationally demanding for such high-dimensional problems due to the required large amount of high-fidelity training data. We propose to address this issue effectively through multi-fidelity ML based surrogates which can use a training dataset consisting of optimally distributed high- and low-fidelity simulations. For forming multi-fidelity surrogates of progressive damage in composite laminates, we combine low-fidelity finite element analysis data obtained using Matzenmiller damage model with Hasin failure criteria and high-fidelity finite element analysis data obtained using three-dimensional continuum damage mechanics based model with P Linde’s failure criteria. It is shown that there is a significant computational advantage to using the multi-fidelity surrogate approach as compared to conventional single-fidelity surrogates. Such computational advantage through optimal data fusion without compromising accuracy becomes crucial for the subsequent data-driven uncertainty quantification and sensitivity analysis of composites involving thousands of realizations. Ply orientations come out to be the most sensitive parameters to matrix damage, fibre damage and reaction force in composite laminates. The degree of uncertainty in the output quantities depend on the input-level stochastic variations. For example, a combined stochastic variation of ±10% in material properties and ±10° in ply orientations lead to 1.85%, 16.98% and 11.24% coefficient of variation in the matrix damage, fibre damage and reaction force respectively. In general, the numerical results obtained based on the efficient data-driven approach strongly suggest that source-uncertainty of composites significantly influences the progressive damage evolution and global mechanical behaviour, leading to the realization of the importance of adopting an inclusive analysis framework considering such inevitable random variabilities.

Surrogate-Assisted Automatic Parameter Adaptation Design for Differential Evolution

In this study, parameter adaptation methods for differential evolution are automatically designed using a surrogate approach. In particular, Taylor series are applied to model the searched dependence between the algorithm’s parameters and values, describing the current algorithm state. To find the best-performing adaptation technique, efficient global optimization, a surrogate-assisted optimization technique, is applied. Three parameters are considered: scaling factor, crossover rate and population decrease rate. The learning phase is performed on a set of benchmark problems from the CEC 2017 competition, and the resulting parameter adaptation heuristics are additionally tested on CEC 2022 and SOCO benchmark suites. The results show that the proposed approach is capable of finding efficient adaptation techniques given relatively small computational resources.

Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction

Over the last decades, computer modeling has evolved from a supporting tool for engineering prototype design to an ubiquitous instrument in non-traditional fields such as medical rehabilitation. This area comes with unique challenges, e.g. the complex modeling of soft tissue or the analysis of musculoskeletal systems. Conventional modeling approaches like the finite element (FE) method are computationally costly when dealing with such models, limiting their usability for real-time simulation or deployment on low-end hardware, if the model at hand cannot be simplified without losing its expressiveness. Non-traditional approaches such as surrogate modeling using data-driven model order reduction are used to make complex high-fidelity models more widely available regardless. They often involve a dimensionality reduction step, in which the high-dimensional system state is transformed onto a low-dimensional subspace or manifold, and a regression approach to capture the reduced system behavior. While most publications focus on one dimensionality reduction, such as principal component analysis (PCA) (linear) or autoencoder (nonlinear), we consider and compare PCA, kernel PCA, autoencoders, as well as variational autoencoders for the approximation of a continuum-mechanical system. In detail, we demonstrate the benefits of the surrogate modeling approach on a complex musculoskeletal system of a human upper-arm with severe nonlinearities and physiological geometry. We consider both, the model’s deformation and the internal stress as the two main quantities of interest in a FE context. By doing so we are able to create computationally low-cost surrogate models which capture the system behavior with high approximation quality and fast evaluations.

Staphylococcus aureus SigS Induces Expression of a Regulatory Protein Pair That Modulates Its mRNA Stability.

SigS is the sole extracytoplasmic function sigma factor in Staphylococcus aureus and is necessary for virulence, immune evasion, and adaptation to toxic chemicals and environmental stressors. Despite the contribution of SigS to a myriad of critical phenotypes, the downstream effectors of SigS-dependent pathogenesis, immune evasion, and stress adaptation remain elusive. To address this knowledge gap, we analyzed the S. aureus transcriptome following transient overexpression of SigS. We identified a bicistronic transcript, upregulated 1,000-fold, containing two midsized genes, each containing single domains of unknown function (DUFs). We renamed these genes SigS-regulated orfA (sroA) and SigS-regulated orfB (sroB). We demonstrated that SigS regulation of the sroAB operon is direct by using in vitro transcription analysis. Using Northern blot analysis, we also demonstrated that SroA and SroB have opposing autoregulatory functions on the transcriptional architecture of the sigS locus, with SroA stimulating SigS mRNA levels and SroB stimulating s750 (SigS antisense) levels. We hypothesized that these opposing regulatory effects were due to a direct interaction. We subsequently demonstrated a direct interaction between SroA and SroB using an in vivo surrogate genetics approach via bacterial adenylate cyclase-based two-hybrid (BACTH) analysis. We demonstrated that the SroA effect on SigS is at the posttranscriptional level of mRNA stability, highlighting a mechanism likely used by S. aureus to tightly control SigS levels. Finally, we demonstrate that the sroAB locus promotes virulence in a murine pneumonia model of infection. IMPORTANCE SigS is necessary for S. aureus virulence, immune evasion, and adaptation to chemical and environmental stressors. These processes are critically important for the ability of S. aureus to cause disease. However, the SigS-dependent transcriptome has not been identified, hindering our ability to identify downstream effectors of SigS that contribute to these pathogenic and adaptive phenotypes. Here, we identify a regulatory protein pair that is a major direct target of SigS, known as SroA and SroB. SroA also acts to stimulate SigS expression at the posttranscriptional level of RNA turnover, providing insight into intrinsically low levels of SigS. The discovery of SroA and SroB increases our understanding of SigS and the S. aureus pathogenesis process.

Traffic Safety Sensitivity Analysis of Parameters Used for Connected and Autonomous Vehicle Calibration

Recently, the number of traffic safety studies involving connected and autonomous vehicles (CAVs) has been increasing. Due to the lack of information regarding the real behaviour of CAVs in mixed traffic flow, traffic simulation platforms are used to provide a reasonable approach for testing various scenarios and fleets. It is necessary to analyse how traffic safety is affected when key parameter assumptions are changed. The current study conducts a sensitivity analysis to identify the parameters used in CAV calibration that have the highest influence on traffic safety. Using a microsimulation-based surrogate safety assessment model approach (SSAM), traffic conflicts were identified, and a ceteris paribus analysis was conducted to measure the effect of gradually changing each parameter on the number of conflicts. Afterwards, a two-at-a-time sensitivity analysis was performed to explore the influence of simultaneously varying two parameters. The results revealed that reaction time, clearance, maximum acceleration, normal deceleration, and the sensitivity factor are key parameters. Studying these parameters two at a time revealed that low maximum acceleration, when combined with other parameters, consistently resulted in the highest number of conflicts, while combinations with short reaction time always yielded the best traffic safety results. This investigation broadens the understanding of CAV behaviour for future implementation for both manufacturers and researchers.

A Surrogate Modeling Approach for Frequency-Reconfigurable Antennas

A novel generalizable surrogate modeling approach is specifically developed for frequency reconfigurable antennas. The generalizable modeling processes is based on the rigorous mathematical derivation, including the solution of a non-linear overdetermined system, the optimization in the complex field, and the interpolation in multi-dimensional continuous space. As a post-processing method, the approach can convert the discrete data of CAD simulation to a surrogate model. Subsequently, a reconfigurable UWB antenna with a tunable notch-band is taken as an example to demonstrate that the surrogate modeling approach is feasible, effective, and precise. It also has the flexible ability to adapt to strict requirements and complicated scenarios. The proposed surrogate model is a good candidate for the interface standard between a reconfigurable antenna and signal processing part in a Cognitive Radio system.

Traffic safety assessment for roundabout intersections using drone photography and conflict technique

Road design deficiencies and improper driver behavior at roundabout intersections may result in traffic bottlenecks, irregular traffic patterns, and potential crashes. Thus, road safety inspection is conducted to identify potential safety hazards and propose safety measures. The traditional safety inspection depends on unreliable traffic collision data visual data collection and superficial analysis. In this regard, surrogate safety assessment approaches are utilized to overcome the limitations found in traditional approaches. This paper employs an innovative surrogate approach for such a process by analyzing videos captured by a drone. A video processing technique is applied to determine the vehicle trajectories and extract conflict points. Accordingly, the conflict data are analyzed in terms of location, direction, and post-encroachment time (PET) as a safety measure to identify potential safety problems related to intersection geometry and driver behavior. This methodology is applied to an intersection case study in New Cairo City, Egypt. The findings of this study confirm the interaction between intersection geometry, drivers’ behavior, and road safety on the examined safety measures.

On predicting crack length and orientation in twill-woven CFRP based on limited data availability using a physics-based, high fidelity machine learning approach

Predicting crack length and orientation in twill woven CFRP plates is notoriously non-trivial due to the interplay of complex physics over multiple length scales. Furthermore, on an industrial scale, a timely yet accurate non-destructive prediction based on a limited amount of data is critical for successful industrial adoption. This paper proposes a physics-assisted surrogate approach, by first measuring electrical conductivities using the Electrical Resistance Charge (ERC) and then developing high-fidelity through-thickness crack models resulting in 42 datasets, and processing the resulting data with a polynomial regression coupled with the leave one out cross validation using physics-based engineered features. The proposed method achieved averaged error of 0.09% and 0.48% for crack length and orientation, respectively, exhibits advantages over the Artificial Neural Network (ANN) in terms of both accuracy and tendency to overfit. This proposed approach paves the way for the maturing of the sought-after real-time Structural Health Monitoring (SHM) of crack length and orientation.

Applications of multi-fidelity multi-output Kriging to engineering design optimization

Surrogate modelling is a popular approach for reducing the number of high fidelity simulations required within an engineering design optimization. Multi-fidelity surrogate modelling can further reduce this effort by exploiting low fidelity simulation data. Multi-output surrogate modelling techniques offer a way for categorical variables e.g. the choice of material, to be included within such models. While multi-fidelity multi-output surrogate modelling strategies have been proposed, to date only their predictive performance rather than optimization performance has been assessed. This paper considers three different multi-fidelity multi-output Kriging based surrogate modelling approaches and compares them to ordinary Kriging and multi-fidelity Kriging. The first approach modifies multi-fidelity Kriging to include multiple outputs whereas the second and third approaches model the different levels of simulation fidelity as different outputs within a multi-output Kriging model. Each of these techniques is assessed using three engineering design problems including the optimization of a gas turbine combustor in the presence of a topological variation, the optimization of a vibrating truss where the material can vary and finally, the parallel optimization of a family of airfoils.

Neighborhood spatial correlations and machine learning classification of fatigue hot-spots in Ti–6Al–4V

The identification of grains with a higher propensity to form a fatigue crack in polycrystalline microstructures is crucial to the design and deployment of materials with increased fatigue resilience. The probability of fatigue crack formation within a grain has typically been quantified with the help of extreme value analyses of Fatigue Indicator Parameters (FIPs). Due to the immense computational cost associated with performing the cyclic crystal plasticity simulations necessary to compute these FIPs, a more efficient strategy to identify the fatigue critical neighborhoods in a microstructure is desirable. In this work, we present a novel surrogate modeling approach to isolate the grains that are likely to exhibit extreme values of the FIPs. This approach significantly extends the previously established Materials Knowledge Systems (MKS) framework to compute neighborhood statistics that capture the salient information about the grain and its neighbors. Variational Bayesian Inference along with Feedforward Neural Networks are utilized to build robust, uncertainty quantified linkages between the local neighborhood statistics and the probability of fatigue crack formation. In doing so, we refine the experimental observations pertaining to the identification of grains that are likely to form a fatigue crack. The approach presented herein is shown to generalize well for both the High Cycle and Transition Fatigue regimes, thereby providing a versatile protocol for multiscale materials design efforts.

Distributionally Robust Surrogate Optimal Control for High-Dimensional Systems

This article presents a novel methodology for tractably solving optimal control and offline reinforcement learning (RL) problems for high-dimensional systems. This work is motivated by the ongoing challenges of safety, computation, and optimality in high-dimensional optimal control. We address these key questions with the following approach. First, we identify a sequence-modeling surrogate methodology that takes as input the initial state and a time series of control inputs and outputs an approximation of the objective function and trajectories of constraint functions. Importantly this approach entirely absorbs the individual state transition dynamics. The sole dependence on the initial state means we can apply dimensionality reduction to compress the model input while retaining most of its information. Uncertainty in the surrogate objective will affect the resulting optimality. Critically, however, uncertainty in the surrogate constraint functions will lead to infeasibility, i.e., unsafe actions. When considering offline RL, the most significant modeling errors will be encountered on out-of-distribution (OOD) data. Therefore, we apply Wasserstein ambiguity sets to “robustify” our surrogate modeling approach subject to worst case out-of-sample modeling errors based on the distribution of test data residuals. We demonstrate the efficacy of this combined approach through a case study of safe optimal fast charging of a high-dimensional lithium-ion battery model at low temperatures.

Multi-fidelity design optimization of solid oxide fuel cells using a Bayesian feature enhanced stochastic collocation

We develop a multi-fidelity surrogate modelling approach to replace the complex and costly physics-based computer models that are often used in the optimization of solid oxide fuel cell (SOFC) performance, or the simplified models that are used in lieu of complex models. We extend multi-fidelity stochastic collocation through a feature engineering step, and eliminate the requirement for the exact low-fidelity output at the inference stage. In contrast to previous approaches, the surrogate model we develop provides detailed spatial information, rather than one or more scalar outputs. This allows for the incorporation of such information into the objective of the optimization study, with the flexibility to choose from more than one objective, such as a minimum, maximum or average. Furthermore, the detailed spatial information can be used for general design purposes, such as ensuring uniformity in reactant and potential distributions. From the results on a 3-d SOFC model, we demonstrate highly accurate predictions of multiple spatially distributed quantities at up to spatial 250,000 locations. The results are superior to state-of-the-art multi-fidelity approaches, particularly for low numbers of high fidelity training points. We use the surrogate model to optimize the SOFC performance with respect to different objectives (including with nonlinear constraints and multiple objectives), with results that are accurate and are obtained in a fraction of the time required for the full model.

Fast uncertainty reduction of chemical kinetic models with complex spaces using hybrid response-surface networks

Response-surface (RS) surrogate approaches permit efficient inverse uncertainty quantification (UQ) of combustion kinetic models, wherein the uncertainty of reaction rates is reduced from observed targets. For kinetic models with parameters characterized by large uncertainty factors, strong nonlinearities, and reaction couplings (e.g., reduced mechanisms of real fuels; such models are referred to be “complex” in this work), the global RS is difficult to approximate, precluding conventional surrogate approaches. This paper proposes a framework that is extendable to such systems, termed Hybrid Response Surface Networks followed by a Stochastic Gradient Descent Ensemble (HRSN-SGDE). This technique focuses on mapping the local RS of just the uncertain spaces in the vicinity of the observed target, referred to as the rate target subspace. Two neural network surrogates are considered: a classifier that predicts the probability of data residing in the rate target subspace and a local RS surrogate which maps the RS of this subspace. A hybrid surrogate loss function is then defined using these surrogates to optimize uncertain rates repeatedly to get an ensemble of solutions representing the constrained rate space. HRSN-SGDE is demonstrated on a complex jet fuel model developed using the hybrid chemistry (HyChem) approach with a low temperature chemistry sub-model using a series of ignition delay times as targets. Results show that the method's local RS objective enables efficient and accurate construction of the surrogates through active learning-based sampling. Also, the unique formulation of the surrogate loss function enables optimization that is robust to suboptimal local minima and faster than evolutionary algorithms by several orders of magnitude. It is shown that HRSN-SGDE method is highly efficacious and suitable to conducting inverse UQ on such complex kinetic models.

Enhancing Computational Accuracy in Surrogate Modeling for Elastic–Plastic Problems by Coupling S-FEM and Physics-Informed Deep Learning

Physics-informed neural networks (PINNs) provide a new approach to solving partial differential equations (PDEs), while the properties of coupled physical laws present potential in surrogate modeling. However, the accuracy of PINNs in solving forward problems needs to be enhanced, and solving inverse problems relies on data samples. The smoothed finite element method (S-FEM) can obtain high-fidelity numerical solutions, which are easy to solve for the forward problems of PDEs, but difficult to solve for the inverse problems. To the best of the authors’ knowledge, there has been no prior research on coupling S-FEM and PINN. In this paper, a novel approach that couples S-FEM and PINN is proposed. The proposed approach utilizes S-FEM to synthesize high-fidelity datasets required for PINN inversion, while also improving the accuracy of data-independent PINN in solving forward problems. The proposed approach is applied to solve linear elastic and elastoplastic forward and inverse problems. The computational results demonstrate that the coupling of the S-FEM and PINN exhibits high precision and convergence when solving inverse problems, achieving a maximum relative error of 0.2% in linear elasticity and 5.69% in elastoplastic inversion by using approximately 10,000 data points. The coupling approach also enhances the accuracy of solving forward problems, reducing relative errors by approximately 2–10 times. The proposed coupling of the S-FEM and PINN offers a novel surrogate modeling approach that incorporates knowledge and data-driven techniques, enabling it to solve both forward and inverse problems associated with PDEs with high levels of accuracy and convergence.

Multi-fidelity modeling and analysis of a pressurized vessel-pipe-safety valve system based on MOC and surrogate modeling methods

A pressurized vessel-pipe-safety valve (PVPSV) combination is a commonly used configuration in nuclear power plants, and a good numerical model is essential for the system design, sizing and performance optimization. However, owing to the large-scale and cross-scale features, it is still a challenge to build a system level numerical model with both high accuracy and efficiency. To overcome this, a novel system level modeling method which can synthesize the advantages of various models is proposed in this paper. For system modeling, the analytical approach, the method of characteristics (MOC) and the surrogate model approach are respectively adopted to predict the dynamics of the pressure vessel, the connecting pipe and the safety valve, and different models are connected through data interfaces. With this system model, dynamic simulations were carried out and both the stable and the unstable system responses were obtained. For the model verification purpose, the simulation results were compared with those obtained from experiments and full CFD simulations. A good agreement and a better efficiency were obtained, verifying the ability of the model and the feasibility of the modeling method proposed in this paper.