Bayesian emulator tackles models with discontinuities efficiently.
problem Uncertainty quantification of models with multiple discontinuities.
method TENSE framework, designed correlation structures, single emulator object.
result Efficient emulation of complex models with multiple discontinuities.
Bayesian approach for learning spatiotemporal systems from noisy data.
problem Efficiently modeling and learning from spatiotemporal dynamical systems with noisy data.
method Hierarchical state-space models with Gaussian process regression for efficient interpolation and training.
result Efficient modeling and learning of spatiotemporal dynamics using Bayesian methods.
Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data -- both local emulators which app…
FaIRGP model improves climate emulation with physical interpretability.
problem Lack of physical interpretability in data-driven emulators.
method Bayesian approach to a data-driven emulator of energy balance equations.
result Demonstrates skillful emulation of global and spatial surface temperatures.
Framework for Bayesian inference using GP emulated MH sampler for noisy likelihoods.
problem Approximate Bayesian inference with limited noisy log-likelihood evaluations.
method Gaussian process emulates MH sampler for log-likelihood evaluations; sequential experimental design selects evaluation points.
result Approximate sampler is sample-efficient and robust to GP assumptions.
This work speeds up DFT simulations using approximate Gaussian processes.
problem Slow DFT simulations due to large data sets.
method Approximate Gaussian processes (sparse variational GP, stochastic variational GP, deep kernel learned GP) to speed up DFT model predictions.
result Calibrated DFT models can predict properties of experimentally unobserved nuclides.
We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex computer models, to examine the structure of complex Bayesian analyses themselves. These techniques facilitate robust Bayesian analyses and/or sensitivity analyses of complex problems, and hence allow global exploration of th…
Bayesian deep learning improves building energy simulation accuracy.
problem Uncertainty in surrogate models for building energy performance.
method Training dropout neural networks and stochastic variational Gaussian Processes.
result Surrogate models reduce errors by up to 30% with uncertainty-aware sampling.
New algorithm speeds up Bayesian UQ for high-dimensional inverse problems.
problem Computational inefficiency in Bayesian inference for high-dimensional inverse problems.
method Deep neural network-based autoencoder for dimension reduction and emulation phase.
result Computational efficiency up to three orders of magnitude with scalable Bayesian UQ.
A new method for training deep Gaussian processes using stochastic imputation.
problem Efficiently training deep Gaussian processes with varying regimes or sharp changes.
method Stochastic imputation to transform DGPs into linked GPs for efficient training.
result The method produces fast and analytically tractable predictions from DGP emulators.
Bayesian calibration improves ABMs for predicting travel patterns.
problem Calibrating ABMs for accurate travel pattern predictions.
method Gaussian Process emulator with deep learning dimensionality reduction for high-dimensional, non-stationary data.
result Improved accuracy in predicting travel patterns using traffic flow data.
Adaptive quadrature improves Bayesian inference through active learning.
problem Efficiently estimating posterior densities in Bayesian inference.
method Sequential node selection using acquisition functions, combining interpolative surrogate models and quadrature rules.
result Positive estimation of marginal likelihood with improved accuracy.
A new BO framework reduces costs by using low-fidelity data.
problem Optimizing expensive experiments with low-fidelity data.
method Developed a multi-fidelity cost-aware Bayesian optimization framework.
result Significantly outperforms state-of-the-art BO methods.
So-called sparse estimators arise in the context of model fitting, when one a priori assumes that only a few (unknown) model parameters deviate from zero. Sparsity constraints can be useful when the estimation problem is under-determined, i.e. when number of model parameters is much higher than the number of data point…
Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters. Traditional Bayesian calibration involves the emulation of the computer model and an additive model discrepancy term using Gaussian processes; inference is then carried out using M…
Method trains emulators to estimate posterior probabilities safely.
problem Uncertainty in slow forward model calculations.
method Trains emulators while estimating posterior probabilities with MCMC, propagating error.
result Demonstrates robust posterior inference for ΛCDM cosmology model. A new emulator connects observables directly from data.
problem Constructing fast and accurate surrogate models for robust predictions.
method Introduces Multiparameter Eigenvalue Problem (MEP) emulator trained with Eigenvector Continuation (EC) and Parametric Matrix Model (PMM) data.
result The MEP emulator can make predictions directly from observables to observables.
Parallelizes active learning for Bayesian inference using Nested Sampler.
problem Expensive likelihood evaluations in complex experiments.
method Uses Nested Sampler to generate nearly-optimal batches of candidates in parallel.
result Comparable accuracy to sequential conditioning with efficient parallelization.
Study compares 29 emulators across 60 test functions and 40 datasets.
problem Comparing the strengths and weaknesses of different emulators.
method Large-scale, fully reproducible comparison using R package duqling.
result Detailed empirical insights into emulator strengths and weaknesses.
A new method uses variational autoencoders to speed up greenhouse gas sensitivity calculations.
problem Computational inefficiency in generating LPDM sensitivities from gas mole fraction observations.
method Developed a convolutional variational autoencoder (CVAE) to emulate LPDM sensitivities in a low-dimensional space.
result The CVAE-based emulator outperforms traditional methods and can be applied to various LPDMs.
Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.
problem Long-term predictions in chaotic spatiotemporal systems are unreliable due to trajectory divergence.
method Diffusion models are used to implicitly estimate the score of an invariant measure, which stabilizes autoregressive emulators by applying denoising during inference.
result Thermalization extends the time horizon of stable predictions by an order of magnitude in chaotic systems.
Researchers develop methods to reduce simulation costs for cardiovascular modeling.
problem High computational cost of high-fidelity simulations in cardiovascular modeling.
method Use low-fidelity approximations, neural networks, and normalizing flows to construct surrogates.
result Validated methods reduce computational cost while maintaining accuracy.
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…
Bayesian neural networks are compressed using feature and weight pruning based on posterior inclusion probabilities.
problem Efficiently compressing Bayesian neural networks to reduce computation cost and improve generalizability.
method Bayesian model selection principles are applied to obtain posterior inclusion probabilities for pruning and feature selection.
result Pruned models show better generalizability on simulated and real-world data.
Optimizes data power control in cell-free networks for better spectral efficiency.
problem Maximizing overall spectral efficiency in cell-free networks with multi-objective optimisation.
method Applied scalable multi-objective Bayesian optimisation to solve convergence-time limitations.
result Improved radio resource management in cell-free networks.
Seq2Seq models speed up epidemic model predictions.
problem Complex epidemic models are computationally expensive.
method Used deep seq2seq models as surrogates for complex models.
result Surrogates predict scenarios up to several thousand times faster.
Neural-network emulators predict sea-level changes due to Antarctic ice melt.
problem High computational cost and time in projecting sea-level changes.
method Built neural-network emulators of sea-level change using GRD effects from future Antarctic Ice Sheet mass change.
result Neural-network emulators are as accurate as baseline machine learning emulators and offer substantial computational efficiency.
New method learns chaotic dynamics from single noisy trajectory.
problem Chaos in complex systems is hard to model accurately with machine learning.
method Adversarial optimal transport objectives to learn summary statistics and emulator from single noisy data.
result Emulators trained with proposed objectives have significantly improved long-term statistical fidelity.
COCA accelerates N-body simulations by correcting ML errors.
problem Computational expense and limited trustworthiness of ML emulations.
method Hybrid framework combining ML and N-body simulator in an emulated frame of reference. result COCA reduces emulation errors with fewer force evaluations.
JANA trains networks to approximate Bayesian models efficiently.
problem Intractable likelihood functions and posterior densities in Bayesian models.
method End-to-end training of three networks: summary, posterior, and likelihood networks.
result JANA provides accurate amortized marginal likelihood and posterior predictive estimation.
Machine learning models accurately predict the state and dynamics of reactive mixing.
problem Accurate prediction of reactive mixing for Earth and environmental science applications.
method Built a high-fidelity numerical model to simulate reactive mixing scenarios. Used 20 different machine learning emulators to classify mixing state and predict three QoIs.
result Ensemble methods and MLP models accurately predict the state of reactive mixing and QoIs, significantly faster than high-fidelity simulations.
Proposes a new PBO method with theoretical guarantees.
problem Optimizing expensive functions in parallel with noisy evaluations.
method Randomized Kriging Believer (KB) based on KB heuristic.
result Randomized KB achieves Bayesian expected regret guarantees.
Generative models emulate climate model outputs for impact assessment.
problem Outdated climate model projections hinder adaptation and mitigation planning.
method Score-based diffusion on a spherical mesh, trained on monthly ESM fields.
result Generative models produce distributions closely matching ESM outputs.
New framework bridges climate science and ML for easier climate model emulation.
problem High computational costs and mistrust of ML methods in climate models.
method Integrating climate science and machine learning perspectives to design easy-to-adopt emulators.
result Demonstrated reliability of emulators designed to address specific tasks.
New method integrates computer models from different disciplines with better predictive performance.
problem Integration of multi-disciplinary computer models with distinct complexities and computation times.
method Developed a linked deep Gaussian process (DGP) method that integrates individual Gaussian process emulators in a network.
result Linked deep Gaussian process emulators outperform standard LGP emulators and single DGPs fitted to the network as a whole.
RADIS uses deep regression to create efficient importance sampling for model inversion and emulation.
problem Efficiently sampling from posterior distributions for model inversion and emulation.
method RADIS uses a deep architecture of nested importance sampling schemes to construct a non-parametric emulator that mimics the posterior distribution.
result RADIS asymptotically converges to an exact sampler under mild conditions and can be used as a surrogate model.
A GPU-based workflow for building physics emulators of hypersonic flows
problem Resolving complex physical phenomena in hypersonic flows
method Fully GPU-based workflow integrating accelerated data generation and neural emulators
result Physics emulators remain reliable beyond their training distribution
Transformers can emulate various algorithms by prompting, proving universality.
problem How to emulate algorithms using fixed-weight Transformers.
method Two modes of in-context algorithm emulation: task-specific and prompt-programmable. Constructing prompts that encode algorithm parameters into token representations.
result Fixed-weight Transformers can emulate a broad class of algorithms via prompts.
Bayesian reflex models AI learning like the autonomic nervous system.
problem Online learning in dynamic AI environments.
method Bayesian online algorithms with belief maintenance, sequential updating, and uncertainty-driven action balancing.
result Unified framework for adaptive AI learning.
A new method for designing accurate emulators using deep learning with interval calibration.
problem Designing accurate emulators for scientific processes with modern machine learning methods.
method Learn-by-Calibrating (LbC) approach based on interval calibration.
result Significant improvements in generalization error over widely-used loss functions.
SPF uses a hierarchical approach to efficiently emulate climate changes.
problem Slow and unstable climate emulation for long horizons.
method Spatiotemporal Pyramid Flows (SPF) model data hierarchically across spatial and temporal scales.
result SPF outperforms flow matching baselines and pre-trained models on ClimateBench.
Bayesian deep ensembles improve prediction accuracy in various settings.
problem Improving prediction accuracy of deep ensembles in out-of-distribution settings.
method Introducing a randomised, untrainable function to each ensemble member, enabling a posterior predictive distribution interpretation.
result Bayesian deep ensembles make more conservative predictions and outperform standard ensembles in various tasks.
Markov Chain Monte Carlo (MCMC) methods have a drawback when working with a target distribution or likelihood function that is computationally expensive to evaluate, specially when working with big data. This paper focuses on Metropolis-Hastings (MH) algorithm for unimodal distributions. Here, an enhanced MH algorithm …
Improved spatial distribution learning with Bayesian transport maps and parametric shrinkage.
problem Learning non-Gaussian spatial distributions with limited training data.
method Proposed ShrinkTM approach using Bayesian transport maps with parametric shrinkage.
result ShrinkTM outperforms existing BTM, especially with few training samples.
New emulator bridges simulators using conditional optimal transport.
problem Bridging simulators with minimal distortion.
method Flow-based approach to learn likelihood transport, COT-FM for optimal matching.
result Emulator accurately captures full correction between simulators.
A deep learning model speeds up computation of numerous implied volatilities.
problem Frequent computation of numerous implied volatilities using iteration methods like Newton-Raphson reaches processing speed limits.
method Emulated Newton-Raphson method using PyTorch and optimized with TensorRT.
result Up to 1,000 times faster than a benchmark implementation of Newton-Raphson.
E&E uses contrastive learning to speed up SBI for high-dimensional systems.
problem Challenges in training high-dimensional emulators for complex systems.
method Contrastive learning for low-dimensional latent embedding and fast emulator.
result Superior performance in non-identifiable parameter estimation tasks.
DCBO optimizes interventions in evolving causal systems.
problem Optimal interventions in time-varying causal systems.
method Combines sequential decision making, causal inference, and GP emulation.
result DCBO identifies optimal interventions faster than competitors.