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.
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.
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.
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.
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…
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.
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.
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.
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 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.
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.
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.
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.
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.
Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulat…
Machine learning speeds up CRTM model predictions for weather forecasting.
problem Insufficient computational efficiency of radiative transfer models.
method Probabilistic neural network emulator of CRTM.
result Emulator predicts brightness temperatures with RMSE < 0.1 K for clear sky conditions.
HierGP improves emulator efficiency for sparse, structured data.
problem Sparse, structured data in expensive simulations.
method Hierarchical shrinkage GP framework with cumulative shrinkage priors.
result HierGP identifies structured sparse features from limited data.
The dynamic emulation of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is examined. Such computer models simulate the evolution of some real-world phenomenon over time, for example models of the climate or the functioning of the human brain. The models we are interest…
Adding inequality constraints (e.g. boundedness, monotonicity, convexity) into Gaussian processes (GPs) can lead to more realistic stochastic emulators. Due to the truncated Gaussianity of the posterior, its distribution has to be approximated. In this work, we consider Monte Carlo (MC) and Markov Chain Monte Carlo (MC…
AutoKE automates embedding physical knowledge into neural networks for complex engineering problems.
problem Complex physical equations in engineering problems.
method AutoKE framework using deep neural networks, equation parsing, automatic differentiation, adaptive weights, and NAS.
result Automatically embeds physical knowledge into neural networks for complex equations efficiently.
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.
Generative model emulates climate model for 100-year forecasts.
problem Challenges in accurately simulating long-term climate data.
method Integrates DYffusion with SFNO for stable, accurate climate simulations.
result Achieves near gold-standard performance for climate model emulation.
Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, the high cost involved hamper reliable and exhaustive simulations. Very often such codes incorporate heuristics that ironical…
A new method for high-dimensional RBDO using stochastic emulators.
problem Efficient RBDO in high-dimensional settings.
method Unified stochastic representation, stochastic emulators, deterministic mapping.
result Significant computational gains in high-dimensional settings.
We introduce CuLE (CUDA Learning Environment), a CUDA port of the Atari Learning Environment (ALE) which is used for the development of deep reinforcement algorithms. CuLE overcomes many limitations of existing CPU-based emulators and scales naturally to multiple GPUs. It leverages GPU parallelization to run thousands …
In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications of ML to emulate existing parameterizations, to develop new parameteri…
ClimART dataset benchmarks ML emulators for atmospheric RT in climate models.
problem Lack of a comprehensive dataset and standardized practices for ML benchmarking in climate models.
method Builds ClimART, a large dataset with over 10 million samples, and presents novel baselines.
result Indicates shortcomings of prior datasets and network architectures.
This paper tackles reliability analysis for stochastic systems using surrogate models.
problem Traditional reliability analysis relies on deterministic models, which are not suitable for stochastic systems with non-repeatable outcomes.
method The paper introduces reliability analysis for stochastic models by using generalized lambda models and stochastic polynomial chaos expansions as surrogate models to lower computational cost.
result The surrogate models enable efficient uncertainty quantification at a lower cost than traditional Monte Carlo simulation.
New method speeds up analysis of computer experiments.
problem Computational infeasibility of direct GP inference for large datasets.
method Adapted Vecchia's ordered conditional approximation to scaled input space.
result Significant performance improvement over existing methods.
We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of sequential Monte Carlo algorithms that are based on building emulator models of the current particle system in a reproducing kernel Hilbert space. We here focus on modelling nonlinear covariance s…
Study uses simulation-based inference to decode brain activity from synthetic stimuli.
problem Reversing the process of brain activity emulation to recover stimuli or their properties.
method Pairing brain emulator with LLMs to learn a probabilistic mapping from brain maps to stimulus parameters.
result LLMs can serve as controllable stimulus generators and parameters can be recovered from brain maps.
Develop a framework to evaluate the reliability of probabilistic emulation of physical systems.
problem Evaluating the reliability of probabilistic forecasts in physical systems.
method Developing a framework to assess the reliability of probabilistic emulation across diverse 2D spatiotemporal systems.
result CRPS-trained ensembles achieve more reliable uncertainties on single-step prediction and autoregressive rollouts.
New method distills cloud models into edge-friendly ones.
problem Cloud-to-edge model compression with limited data exchange.
method Two-step workflow of deprivatization and distillation.
result Outperforms previous state-of-the-art approaches on various benchmarks.
Emulator speeds up landslide run-out modeling sensitivity analysis.
problem Computational challenges in assessing landslide run-out model sensitivity.
method Gaussian process emulation integrated into r.avaflow.
result Strong interactions detected between friction coefficients and release volume.
Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-fidelity, finite-element-based numerical model is built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate …
Numerical models based on physics represent the state-of-the-art in earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest-generation computers, reducing the ability of …
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.