MF-GLaM models improve stochastic simulator emulation with multifidelity data.
problem Challenging to emulate stochastic simulators' full conditional probability distribution.
method Proposes MF-GLaMs to efficiently emulate HF stochastic simulators using LF data.
result MF-GLaMs achieve improved accuracy or comparable performance at reduced cost.
Estimates reliability of nuclear fuel using advanced modeling techniques.
problem Determining the reliability of TRISO-coated particle fuel, which has small failure probabilities and expensive computational models.
method Coupled active learning, multifidelity modeling, and subset simulation.
result Multifidelity modeling strategies consistently reduce the number of high-fidelity model calls.
A new method uses multifidelity Gaussian process regression to solve nonlinear PDEs.
problem Efficiently solving nonlinear PDEs using kernel methods.
method Proposes a kernel learning approach based on cokriging for multifidelity simulations.
result Demonstrates improved performance on the Burgers' equation.
A new method combines multifidelity techniques to improve model accuracy with limited data.
problem Improving model accuracy with sparse accurate observations and stochastic simulation models.
method Combines bifidelity and CoKriging methods to estimate empirical statistics and construct a Gaussian process.
result Preserves linear physical constraints up to an error bound, leading to accurate model construction.
New method uses low-fidelity simulations to efficiently infer parameters of high-fidelity models.
problem Challenges in inferring parameters of computationally expensive high-fidelity models.
method Multifidelity simulation-based inference using transfer learning and adaptive selection of high-fidelity parameters.
result Significant reduction in the number of high-fidelity simulations required for inference.
Paper proposes a new method to estimate transportation technology risks.
problem Estimating risks associated with new transportation technologies.
method Multifidelity simulation combining low-fidelity and high-fidelity data.
result Allows evaluation of new technologies at conception stage.
MFNets constructs efficient multifidelity surrogates from diverse information sources.
problem Creating accurate surrogates from multiple, potentially costly or inaccurate data sources.
method Directed acyclic graph of connections, gradient-based minimization of least squares objective, flexible information source structure.
result Error reduction by orders-of-magnitude, especially in low-data scenarios.
New approach uses low-fidelity data to train ML models efficiently.
problem Training ML models with scarce high-fidelity data leads to high variance and poor generalization.
method Multifidelity linear regression using approximate control variates.
result Multifidelity training achieves similar accuracy with reduced high-fidelity data.
Efficiently estimates rare events using multifidelity modeling.
problem Estimating rare events with computationally expensive models.
method Active learning with multifidelity modeling, adapting the number of high-fidelity simulations based on problem complexity and desired accuracy.
result Significantly reduced the number of high-fidelity model calls while maintaining accuracy.
Paper introduces a bandit-learning method for multifidelity approximations.
problem Efficiently using data of varying fidelities in scientific computation.
method Formulates multifidelity approximation as a modified stochastic bandit problem and proposes AETC algorithm.
result Established optimality of AETC algorithm for multifidelity approximation.
New methods combine low and high-fidelity data for accurate surrogate modeling.
problem Challenges in surrogate modeling for high-dimensional outputs with limited training data.
method Projection-based multifidelity linear regression methods integrating low-fidelity and high-fidelity data.
result Multifidelity methods achieve up to 12% improvement in median accuracy compared to single-fidelity methods.
This thesis explores adaptive sampling techniques for creating efficient surrogate models.
problem High computational cost of numerical simulations.
method Adaptive sampling techniques for Kriging metamodels.
result Comprehensive comparison of adaptive sampling techniques for Kriging.
Gradient-enhanced deep GPs improve multifidelity model accuracy.
problem Improving accuracy in multifidelity models using gradient data.
method Extending deep Gaussian processes to incorporate gradient data.
result Gradient-enhanced deep GP outperforms other models in predicting aerodynamic coefficients.
RAAL optimizes black box function optimization with multifidelity models.
problem Time-consuming and unfeasible black box optimization.
method Resource Aware Multifidelity Active Learning (RAAL) for efficient optimization.
result RAAL optimizes black box function optimization with multifidelity models.
A cost-effective framework for gradual domain adaptation using multifidelity.
problem Degrading prediction performance due to large domain distance.
method Combines multifidelity and active domain adaptation.
result Improves prediction performance with reduced sample cost.
Entropy-based GP adaptive design improves failure probability estimation.
problem Limited accuracy in failure probability estimation due to model evaluation costs.
method Entropy-based Gaussian process (GP) adaptive design combined with multifidelity importance sampling (MFIS).
result More accurate failure probability estimates and higher confidence.
Two multifidelity trust-region methods use low-fidelity models for efficient optimization.
problem Efficiently solving complex optimization problems with limited data.
method Sketched Trust-Region (STR) and SVD Trust-Region (SVDTR) methods using low-fidelity models.
result Potential gain in efficiency demonstrated through numerical examples.
CoPhIK uses physics-informed Kriging to improve data-model convergence.
problem Improving data-model convergence in multifidelity problems.
method Physics-informed CoKriging (CoPhIK) combines PhIK and a parameterized GP to model discrepancies.
result CoPhIK reduces optimization cost and satisfies physical constraints up to an error bound.
BAMS uses Bayesian sampling to discover AV failures more efficiently and accurately.
problem Discovering potential failure cases in autonomous vehicles efficiently and accurately.
method Bayesian adaptive multifidelity sampling (BAMS) prioritizes exploration of low performance regions.
result BAMS discovers 10 times more issues than traditional methods with narrower rate estimates.
Method for creating synthetic multi-fidelity data sets.
problem Lack of representative synthetic datasets for multifidelity optimisation benchmarks.
method Systematic generation of synthetic fidelities from preexisting datasets.
result Allows systematic investigation of lower fidelity proxies' influence.
A method to improve surrogate model accuracy using multiple fidelity models.
problem Efficiently combining models of varying accuracy and computational cost.
method Multifidelity Gaussian process models and leave-one-out cross-validation.
result Reduced LOO-CV error at the highest fidelity through adaptive learning.
Enhances prediction with Gaussian Processes using multifidelity data.
problem Improving prediction accuracy with limited high-fidelity data.
method Combining high-fidelity and low-fidelity data through Gaussian Processes and manifold embeddings.
result Gaussian Processes can effectively approximate complex high-fidelity functions using low-fidelity data and additional functions.
Efficiently estimates material parameter space with multifidelity Gaussian process modeling.
problem Estimating a region of material parameter space with similar precipitate shapes.
method Multifidelity Gaussian process modeling to reduce computational cost.
result Significant reduction in sampling cost for accurate LER estimation.
A new method for reducing model complexity using neural active manifolds.
problem Uncertainty quantification in computationally expensive models.
method Autoencoders and surrogate models to discover a neural active manifold.
result Neural active manifolds reduce model variance in multifidelity sampling.
Bayesian optimization for binomial outputs with multifidelity.
problem Optimizing functions with binomial outputs that don't fit Gaussian process assumptions.
method General Gaussian process model for binomial data, Expected Improvement acquisition function, heuristic sample selection.
result Improves optimization performance for binomial target functions.
This work improves surrogate models using low-fidelity data to enhance accuracy and efficiency.
problem Limited training data makes high-fidelity models unreliable.
method Uses low-fidelity data to augment input space and condition high-fidelity models.
result Increased predictive accuracy and reduced computational cost compared to existing methods.
mfEGRA uses active learning to efficiently locate failure boundaries in reliability analysis.
problem Prohibitive cost of reliability analysis using Monte Carlo sampling for high-fidelity models.
method Develops a multifidelity active learning method using data-driven adaptively refined surrogates.
result Significant computational savings (46-48%) compared to single-fidelity EGRA.
Automated HPO design using Bayesian optimization and benchmarking.
problem Designing effective hyperparameter optimization algorithms is manual and lacks systematic understanding.
method Formalized space of HPO candidates, Bayesian optimization for search, ablation analysis.
result Simple configurations can perform well in HPO, especially with right parameters.
GAR generalizes autoregression for efficient multi-fidelity fusion.
problem Efficiently combining low-fidelity and high-fidelity simulation results.
method Generalized autoregression (GAR) using tensor formulation and latent features.
result GAR outperforms state-of-the-art methods with a large margin in RMSE.
This work presents a technique for statistically modeling errors introduced by reduced-order models. The method employs Gaussian-process regression to construct a mapping from a small number of computationally inexpensive `error indicators' to a distribution over the true error. The variance of this distribution can be…
Proposes a stratified sampling method for high-dimensional models using neural active manifolds.
problem Uncertainty propagation in computationally expensive models with many inputs.
method Neural active manifolds for nonlinear dimensionality reduction, followed by stratification in the reduced space.
result Effective variance reduction in high-dimensional models using stratified sampling.
New method improves neural architecture search by optimizing for both performance and diversity.
problem Traditional multi-objective NAS fails to address practical constraints and niches.
method Formulated as quality diversity optimization, introduces multifidelity optimizers.
result Quality diversity NAS outperforms multi-objective NAS in quality and efficiency.
Enhanced Gaussian process regression for multi-fidelity data fusion.
problem Combining data of varying fidelity levels for accurate predictions.
method Gradient-enhanced Cokriging method (GE-Cokriging) for QoI and its gradients.
result GE-Cokriging outperforms conventional multi-fidelity Cokriging in predicting QoI and gradients.
Enhances multi-fidelity modeling with DGPs for different input domains.
problem Improving prediction accuracy with multi-fidelity models using different input domains.
method Extends Deep Gaussian Processes (DGPs) to handle different input domains for high and low-fidelity models.
result Demonstrates improved performance on real-world physical problems.
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…
A new framework connects machine learning models with simulation models efficiently.
problem Interpreting complex machine learning models for real-world applications.
method Model-bridging framework using kernel mean embeddings.
result Simulations and machine learning models can be used together without high computational costs.
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.
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.
Develops EB for implicit likelihoods using simulators.
problem Traditional EB assumes tractable likelihoods, SBEB handles implicit likelihoods.
method Simulation-based empirical Bayes (SBEB) connects nonparametric EB to SBI, iteratively refining EB estimates.
result SBEB improves accuracy over SBI with fixed priors.
Study assesses market simulation metrics to highlight discrepancies between real and simulated markets.
problem Lack of fidelity in market simulation methods leads to discrepancies between real and simulated market data.
method Surveyed and applied a set of reference metrics to real and simulated market data.
result Significant discrepancies remain between real and simulated markets.
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.
New method improves sample-efficiency in neural posterior estimation using simulator gradients.
problem High-fidelity posterior estimation with complex physical simulations is time-consuming.
method Neural Posterior Estimation (NPE) with differentiable simulators and gradient information.
result Improves sample-efficiency in posterior density estimation.
Paper presents a new validation method for simulation workflow.
problem Validation of simulation workflow is challenging and domain-dependent.
method Empirical learning-based validation procedure using AHP and machine learning.
result Validation procedure is semi-automated and efficient.
Simulates risk-neutral markets using neural spline flows.
problem Creating realistic risk-neutral market simulations.
method Developed a low-dimensional martingale representation and used neural spline flows for sampling.
result The calibrated simulator is closest to historical data with respect to Kullback-Leibler divergence.
Generative Adversarial Networks simulate elevator group control without extensive data.
problem Lack of historical real-world data for system testing.
method Used GANs to generate simulation data for a multi-car elevator system.
result GANs can be used as substitutes for expensive simulation runs, but fine-tuning is needed.
Taxonomy for ML in simulations, covering patterns and algorithms.
problem Enhancing simulations using machine learning.
method Presentation of eight patterns and three algorithmic areas.
result Catalog of activities and patterns for ML integration in simulations.
Enhances SBI accuracy with multilevel Monte Carlo for expensive simulators.
problem Limited accuracy in SBI due to expensive simulators.
method Multilevel Monte Carlo techniques for cost-effective SBI.
result Significant enhancement in SBI accuracy with fixed computational budget.