To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making. In this paper, we study the two-stage stochastic programming for complex systems with unknown response estimated by simulation. We introduce the global-local metamodel assisted t…
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48 results for “simulation metamodeling”
New deep learning model optimizes energy use in buildings.
problem Optimizing energy use and comfort in large buildings.
method Transformer-based metamodel trained with simulation and sensor data, calibrated with CMA-ES, optimized with multi-objective algorithms.
result Optimal settings reduce energy loads while maintaining thermal comfort and air quality.
We analyze the accuracy of traffic simulations metamodels based on neural networks and gradient boosting models (LightGBM), applied to traffic optimization as fitness functions of genetic algorithms. Our metamodels approximate outcomes of traffic simulations (the total time of waiting on a red signal) taking as an inpu…
E-QRGMM accelerates uncertainty quantification in simulations.
problem Challenges in covariate-dependent uncertainty quantification.
method Integrates cubic Hermite interpolation with gradient estimation.
result Substantially improves computational efficiency and accuracy.
We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic samplers, in particular with heavy-tailed simulation noise and low signal-to-no…
MetaStackVis aids in choosing better metamodels for stacking ensembles.
problem Difficulty in selecting optimal metamodels for stacking ensembles.
method Interactive visualization tool to explore and compare different metamodels.
result Alternative metamodels significantly improve stacking ensemble performance.
The computational effort for the evaluation of numerical simulations based on e.g. the finite-element method is high. Metamodels can be utilized to create a low-cost alternative. However the number of required samples for the creation of a sufficient metamodel should be kept low, which can be achieved by using adaptive…
New method improves stochastic kriging for high-dimensional simulations.
problem High-dimensional simulation models require prohibitive sample sizes and computational costs.
method Tensor Markov kernels and sparse grid experimental designs.
result Sample complexity grows only slightly with dimensionality, improving accuracy and efficiency.
We present a novel technique for assessing the dynamics of multiphase fluid flow in the oil reservoir. We demonstrate an efficient workflow for handling the 3D reservoir simulation data in a way which is orders of magnitude faster than the conventional routine. The workflow (we call it "Metamodel") is based on a projec…
Surrogate models improve tidal model calibration efficiency.
problem Efficiently calibrate complex tidal models for climate change scenarios.
method Proposes two surrogate-based methods to replace complex models: PODEn3DVAR and POD-PCE-3DVAR.
result Both methods show superior convergence and robustness to noise compared to classical 3DVAR.
We report on an empirical study of the main strategies for quantile regression in the context of stochastic computer experiments. To ensure adequate diversity, six metamodels are presented, divided into three categories based on order statistics, functional approaches, and those of Bayesian inspiration. The metamodels …
BayCANN uses ANN to speed up Bayesian calibration in health sciences.
problem Bayesian calibration's practical and computational burdens in health decision sciences.
method BayCANN trains an ANN metamodel to calibrate parameters probabilistically, comparing accuracy and speed to direct Bayesian calibration.
result BayCANN is more accurate and faster than direct Bayesian calibration methods.
Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimationstat.ML
Adaptive batching improves Gaussian process surrogates for noisy level set estimation.
problem Learning the level set of noisy simulator responses.
method Developed four novel adaptive batching schemes for Gaussian process metamodels.
result Adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate. Starting with the premise that simulation-based approaches are not affordable for such problems, and that the most-probab…
Paper develops efficient Bayesian inference for enzymatic SRNs with LNA metamodel.
problem Bayesian inference for nonlinear SDE-based mechanistic models with partial observations and measurement errors.
method Interpretable Bayesian updating LNA metamodel and efficient posterior sampling.
result Proposed approach demonstrates promising performance in empirical studies.
Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computationsq-fin.CP
Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio valuation involved in CVA compu…
We investigate two new strategies for the numerical solution of optimal stopping problems within the Regression Monte Carlo (RMC) framework of Longstaff and Schwartz. First, we propose the use of stochastic kriging (Gaussian process) meta-models for fitting the continuation value. Kriging offers a flexible, nonparametr…
A green simulation-assisted reinforcement learning method for biomanufacturing.
problem Complexity, high variability, lead time, and limited historical data in biopharmaceutical manufacturing.
method Quantifies model risk, uses posterior distribution, and selectively reuses simulation data.
result Demonstrates promising performance in online learning and decision making.
We describe a method for Bayesian optimization by which one may incorporate data from multiple systems whose quantitative interrelationships are unknown a priori. All general (nonreal-valued) features of the systems are associated with continuous latent variables that enter as inputs into a single metamodel that simult…
We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation. Many transportation system analysis tasks are formulated as an optimization problem - such as optimal control problems in intelligent transportation systems and long term urban planning. Often transportation…
In the field of structural reliability, the Monte-Carlo estimator is considered as the reference probability estimator. However, it is still untractable for real engineering cases since it requires a high number of runs of the model. In order to reduce the number of computer experiments, many other approaches known as …
We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related problems. This is useful when optimizing the output of a stochastic simulator that fails to provide derivative information, for which Bayesian opt…
We present a regression technique for data-driven problems based on polynomial chaos expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ), where it is typically used to replace a runnable but expensive computational model subject to random inputs with an inexpensive-to-evaluate po…
This paper proposes an active learning-based Gaussian process (AL-GP) metamodelling method to estimate the cumulative as well as complementary cumulative distribution function (CDF/CCDF) for forward uncertainty quantification (UQ) problems. Within the field of UQ, previous studies focused on developing AL-GP approaches…
A new framework interprets machine learning models using Gaussian processes.
problem Interpreting complex machine learning models.
method Gaussian process metamodeling to capture response surfaces.
result Maximizing likelihood function for variable importance.
Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to running an expensive-to-evaluate computational model (e.g. a finite element model). In this respect simulation methods, whic…
LMGPs extend GPs to handle mixed data, offering better accuracy and interpretability.
problem Handling mixed data types (quantitative and qualitative) in metamodeling.
method Introduce LMGPs that learn a latent manifold for qualitative inputs, using a low-rank linear map.
result LMGPs outperform existing methods in accuracy and versatility.
This paper presents an automatic approach for selecting optimal meta-models for sensitivity analysis in complex systems.
problem Efficient surrogate models for high-dimensional problems in virtual prototyping.
method Automatic selection of meta-models, variable space reduction, and advanced sensitivity measures.
result Optimal meta-models and subspace identification for accurate probabilistic analysis.
An innovative adaptive kriging approach for efficient binary classification of mechanical problemscs.LG
Kriging is an efficient machine-learning tool, which allows to obtain an approximate response of an investigated phenomenon on the whole parametric space. Adaptive schemes provide a the ability to guide the experiment yielding new sample point positions to enrich the metamodel. Herein a novel adaptive scheme called Mon…
Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objective optimization problems in Bayesian multiobjective optimization is scarce. Scalarizing functions ca…
Probabilistic Bisection Algorithm performs root finding based on knowledge acquired from noisy oracle responses. We consider the generalized PBA setting (G-PBA) where the statistical distribution of the oracle is unknown and location-dependent, so that model inference and Bayesian knowledge updating must be performed s…
Locally adaptive interpretable regression improves linear regression's predictability.
problem Linear regression's predictability is limited; it lacks adaptability.
method Locally adaptive interpretable regression (LoAIR) uses neural networks to predict percentile of a Gaussian distribution for regression coefficients.
result LoAIR achieves comparable or better predictive performance than state-of-the-art baselines.
Paper reduces hyperparameters in mixed-categorical Gaussian processes for green aircraft optimization.
problem High-dimensional mixed-categorical Gaussian processes with many hyperparameters.
method Innovative dimension reduction algorithm using partial least squares regression.
result Significant reduction in fuel consumption (439 kg) for a green aircraft.
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysiscs.LG
In this paper, we introduce a physics-driven regularization method for training of deep neural networks (DNNs) for use in engineering design and analysis problems. In particular, we focus on prediction of a physical system, for which in addition to training data, partial or complete information on a set of governing la…
Enhanced kernel framework for advanced data forecasting.
problem Complex mechanical behaviors and timefrequency dynamics in aircraft systems.
method Frequency-aware surrogate modeling with SMT kernels, extending kernel types and incorporating derivatives.
result Improved accuracy in forecasting CO2 concentrations and airline passenger traffic.
RAPTOR-GEN accelerates digital twin development for biopharmaceuticals.
problem Lack of agility in rapid, on-demand production of biotherapeutics due to bioprocess complexity.
method Bayesian learning framework based on a multi-scale probabilistic knowledge graph (pKG) and stochastic differential equations (SDE).
result RAPTOR-GEN accelerates intelligent digital twin development from sparse and heterogeneous experimental data.
Study uses generative models to assess credit risk and determine loan sizes in e-commerce supply chain finance.
problem Credit risk assessment and loan size determination for small- and medium-sized sellers in e-commerce supply chain finance.
method Proposes a unified framework using Quantile-Regression-based Generative Metamodeling (QRGMM) integrated with Deep Factorization Machines (DeepFM) to capture complex covariate interactions in e-commerce sales data.
result Validates the model's efficacy for credit risk assessment and loan size determination on synthetic and real-world data.
New method for mixed-variable GSA improves material design efficiency.
problem Designing materials with both quantitative and qualitative variables.
method Integrates LVGP with Sobol' analysis for mixed-variable GSA.
result Accelerates exploration of novel MOF candidates in combinatorial design spaces.
We examine several aspects of explicability of a classification system built from neural networks. The first aspect is the pairwise explicability, which is the ability to provide the most accurate prediction when the range of possibilities is narrowed to just two. Next we consider explicability in development, which me…
ASADG improves data generation for accurate surrogate modeling of complex physical problems.
problem Training surrogate models on imbalanced data leads to inaccurate predictions.
method ASADG iteratively adds input data to improve the representation of the response manifold.
result ASADG generates more representative input data compared to LHS for better model accuracy.
StackGenVis simplifies ensemble learning by visualizing model selection and performance.
problem Complexity in choosing and combining models for stacking ensemble learning.
method Visual analytics system that dynamically adapts performance metrics, manages data instances, selects algorithms, and measures predictive performance.
result Reduces complexity of stacking by removing overpromising and underperforming models.
The study develops a logic reasoner to verify MS case management specifications.
problem Ensuring correctness and solvability of abstract clinical work products.
method Developed a logic reasoner using UML class and state diagrams, SPIN, and ODM.
result Formal representations of UML diagrams proved consistent for MS case management.
The simulator is an R package that streamlines the process of performing simulations by creating a common infrastructure that can be easily used and reused across projects. Methodological statisticians routinely write simulations to compare their methods to preexisting ones. While developing ideas, there is a temptatio…
PAMS is a Python-based platform for simulating artificial markets.
problem Simulating complex market behaviors for research and education.
method Developed as a Python-based simulator with deep learning integration.
result Demonstrated effectiveness through agent price prediction studies.
The interpretability of machine learning, particularly for deep neural networks, is crucial for decision making in real-world applications. One approach is replacing the un-interpretable machine learning model with a surrogate model, which has a simple structure for interpretation. Another approach is understanding the…
Smartfluidnet accelerates Eulerian fluid simulation with neural networks.
problem Current neural network methods for Eulerian fluid simulation lack flexibility and generalization.
method Smartfluidnet automates model generation and dynamic switching to meet user requirements.
result Smartfluidnet achieves 1.46x and 590x speedup compared to state-of-the-art models, with better simulation quality.
Proposes a new simulator for complex arrival processes.
problem Modeling and simulating complex arrival processes with non-stationary and multi-dimensional rates.
method Integrates Monte Carlo and GANs to model a broad class of arrival processes.
result Consistent and efficient estimation of the simulator using Wasserstein distance.
ACE improves GBI for simulators by approximating cost functions, making inference more efficient.
problem Inference for misspecified simulators is overly restrictive.
method Amortized cost estimation (ACE) for Generalized Bayesian Inference (GBI).
result ACE provides accurate cost predictions and more efficient inference.