Machine learning combines high- and low-fidelity models for efficient uncertainty quantification and optimization.
problem Efficiently combining high- and low-fidelity models for uncertainty quantification and optimization.
method Machine learning-based multi-fidelity methods for uncertainty quantification and optimization.
result Unified perspective on multi-fidelity priors for optimization.
Proposes a multi-fidelity machine learning strategy integrating low-fidelity deterministic and high-fidelity Bayesian models.
problem Addressing the accuracy-efficiency trade-off in machine learning with scarce high-fidelity data.
method Integrates a non-probabilistic regression model for low-fidelity with a Bayesian model for high-fidelity, trained in a staggered scheme.
result Achieves comparable performance in mean and uncertainty estimation with reduced training time and effective mitigation of overfitting.
Enhances inverse design optimization with machine learning and reduced fidelity simulations.
problem Limited compute resources in inverse design optimization.
method Synergy of multi-fidelity simulations, machine learning, and search space reduction.
result Significant computational resource savings and improved optimization performance.
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models. This arises in both fundamental machine learning procedures such as Bayesian optimization, as well as mor…
A cost-efficient method for hyperparameter tuning using multi-fidelity Bayesian optimization.
problem Expensive hyperparameter tuning with limited knowledge transfer methods.
method Amortized Auto-Tuning (AT2) framework for multi-task, multi-fidelity Bayesian optimization.
result AT2 leads to the best hyperparameter recommendation and is more cost-efficient.
Machine learning techniques typically rely on large datasets to create accurate classifiers. However, there are situations when data is scarce and expensive to acquire. This is the case of studies that rely on state-of-the-art computational models which typically take days to run, thus hindering the potential of machin…
Paper optimizes multi-fidelity function with fast learning rates.
problem Optimizing a locally smooth function with limited budget and varying fidelity approximations.
method Kometo algorithm that achieves simple regret rates without knowing function smoothness or fidelity assumptions.
result Kometo algorithm outperforms previous methods empirically.
FNO model predicts GCS pressure fields with 81% less data, even with limited high-fidelity data.
problem Accurate prediction of complex physical behaviors in large-scale 3D geological carbon storage problems with limited data.
method Multi-fidelity Fourier Neural Operator (FNO) for efficient training with multi-fidelity datasets.
result Multi-fidelity FNO model predicts pressure fields with reasonable accuracy even with limited high-fidelity data.
RMFGP combines multi-fidelity models for efficient uncertainty quantification.
problem Efficiently infer quantities of interest with limited high-fidelity data.
method Rotated multi-fidelity Gaussian process with dimension reduction and Bayesian active learning.
result RMFGP model improves accuracy and efficiency in high-dimensional problems.
Study builds ML models to predict fuel properties accurately.
problem Accurate prediction of liquid fuel properties over a wide range of conditions.
method Used Gaussian Processes and probabilistic conditional generative learning to train ML models on fuel density data.
result ML models can predict fuel properties accurately across various pressure and temperature conditions.
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.
FGPR uses averaging and SGD for federated GP regression, excelling in personalization and multi-fidelity modeling.
problem Privacy-preserving multi-fidelity data modeling and personalization.
method Federated Gaussian process framework with averaging and SGD for local computations.
result FGPR converges to a critical point of the full log-likelihood function, excels in personalization and multi-fidelity modeling.
Generative AI improves surrogate models by blending LF and HF data.
problem Data scarcity between high-fidelity and low-fidelity simulations.
method Probabilistic multi-fidelity surrogate framework using generative transfer learning.
result The model achieves HF accuracy with fewer HF evaluations.
Combines multi-fidelity and asynchronous batch methods for faster experimental design.
problem Designing optimal experimental setups for battery performance.
method Algorithm combining multi-fidelity and asynchronous batch Bayesian Optimization.
result Algorithm outperforms single-fidelity batch and multi-fidelity sequential methods.
Enhances Gaussian process regression with multi-fidelity models and active subspaces for high-dimensional problems.
problem Data scarcity and high-dimensional input spaces with low intrinsic dimensionality.
method Employ Gaussian processes in a Bayesian setting, augmenting with low-fidelity models, and exploiting active subspaces.
result Improves model accuracy through multi-fidelity Gaussian process regression with active subspaces.
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.
Efficiently predicts high-fidelity PDE solutions using multi-fidelity Gaussian processes.
problem Expensive high-fidelity solutions for PDEs on discretized domains.
method Multi-Fidelity High-Order Gaussian Process (MFHoGP) that integrates multi-fidelity examples and scales to large numbers of outputs.
result Significantly reduces the cost of high-fidelity PDE solutions through efficient Gaussian process modeling.
The paper compares multi-fidelity methods for Gaussian process surrogates in physics.
problem Limited availability of data due to expensive simulations.
method Extending non-linear autoregressive methods to multi-fidelity models and incorporating delay terms.
result Multi-fidelity methods generally have smaller prediction error for the same computational cost.
This paper reviews Gaussian process-based multi-fidelity techniques for different fidelity relationships.
problem Combining accurate and cheap models for complex system design.
method Gaussian process-based multi-fidelity modeling techniques for varying fidelity relationships.
result Comparison of techniques on analytical and aerospace engineering problems.
CAGES optimizes expensive RL problems by efficiently learning gradients from multiple sources.
problem Optimizing expensive-to-evaluate functions in high-dimensional spaces.
method Cost-Aware Gradient Entropy Search (CAGES) for multi-fidelity Bayesian optimization.
result Significant performance improvements on synthetic and RL benchmark problems.
This paper presents a method to efficiently estimate rare event probabilities using a combination of high and low-fidelity models.
problem Estimating the probability of failure for complex systems using high-fidelity models is expensive and inaccurate for rare events.
method The paper introduces a multi-fidelity surrogate modeling strategy using active learning and subset simulation to merge high and low-fidelity models.
result The method significantly reduces computational cost while maintaining high accuracy in estimating rare event probabilities.
Conditional DGP learns effective kernels from low-fidelity data.
problem Learning effective kernels for multi-fidelity regression.
method Conditional DGP with moment matching for implicit kernel approximation.
result Effective kernels are learned from lower-fidelity data, improving multi-fidelity regression.
New method optimizes aircraft design with reduced computation using multi-fidelity models.
problem Efficiently solve complex aircraft design problems with limited computational resources.
method Proposes novel multi-fidelity selection strategies that consider both objective and constraint information.
result Shows 86% to 200% more constraint compliant solutions with a limited budget. Investigates long-term performance of multi-fidelity Bayesian optimization.
problem Potential long-term under-performance of multi-fidelity Bayesian optimization.
method Simple benchmark study to investigate long-term performance.
result Under-performance of multi-fidelity Bayesian optimization in certain scenarios.
Improves Bayesian optimization for multi-fidelity functions.
problem Inefficient estimation of black-box functions due to ignored or oversimplified correlations between fidelities.
method Proposes DNN-MFBO using deep neural networks to capture complex relationships between fidelities.
result Shows significant improvement in optimization performance on synthetic and real-world datasets.
Enhanced multi-fidelity models improve digital twin accuracy and uncertainty quantification.
problem Lack of detailed application-specific data and inaccurate sensor data hinder surrogate model learning for digital twins.
method Proposes a multi-fidelity surrogate model framework integrating PCFE and GP, and deep-HPCFE with auto-regression schemes.
result Demonstrates improved accuracy and uncertainty quantification in digital twin systems.
New method tackles constrained optimization in multi-fidelity Bayesian optimization.
problem Efficiently identifying feasible regions in constrained optimization problems.
method Proposes CMFBO method with novel acquisition functions.
result Demonstrates effectiveness on synthetic problems and real-world ICF and joint design problems.
Bayesian optimization speeds up bioprocess development across scales.
problem Costly and complex bioprocess development across scales and biocatalyst selection.
method Multi-fidelity batch Bayesian optimization framework integrating Gaussian Processes and mixed-variable optimization.
result Reduction in experimental costs and increased yield in bioprocess optimization.
New method for reliability analysis using multi-fidelity models.
problem Reliability analysis of complex systems with high computational costs.
method Adaptive Multi-fidelity Gaussian Process for Reliability Analysis (AMGPRA) with collective learning function (CLF).
result AMGPRA achieves similar or higher accuracy with reduced computational costs compared to state-of-the-art methods.
New method improves multi-fidelity Bayesian optimization by accounting for local correlations and varying noise.
problem Existing multi-fidelity Bayesian optimization methods assume global correlation and constant noise, which limits performance.
method Proposes an MF emulation method that learns noise models for each data source and leverages locally correlated LF sources.
result Improves performance of multi-fidelity Bayesian optimization by accounting for local correlations and varying noise.
This work improves surrogate models for balancing accuracy and cost in multi-fidelity methods.
problem Balancing accuracy and computational cost in multi-fidelity methods.
method Develops context-aware surrogate models for multi-fidelity importance sampling and Bayesian inverse problems.
result Context-aware surrogate models can lead to runtime speedups of up to one order of magnitude.
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and …
Auto-PyTorch automates deep learning by optimizing neural architectures and hyperparameters.
problem Automated deep learning for tabular data with robust and efficient optimization.
method Combines multi-fidelity optimization, portfolio construction, and warmstarting with ensembling.
result Achieves state-of-the-art performance on tabular benchmarks.
Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyperparameter settings remains a bottleneck. Multi-fidelity optimization promises relief using cheaper p…
Neural network fusion reduces data acquisition costs for multi-fidelity sources.
problem Reducing cost in acquiring information from multiple data sources with varying fidelity.
method Employing a novel neural network architecture for nonlinear manifold learning of multi-fidelity data.
result Our approach provides high predictive power and quantifies various sources uncertainties.
Bayesian method maps high-dimensional inputs to lower dimensions for efficient multi-fidelity Gaussian Process modeling.
problem Efficiently modeling high-dimensional inputs with low-dimensional latent variables for multi-fidelity Gaussian Processes.
method Bayesian approach with orthonormal projection matrix inference using Markov Chain Monte Carlo (MCMC) and Geodesic Monte Carlo sampling.
result Optimal transformations identified that improve computational efficiency in multi-fidelity Gaussian Process modeling.
A new MCMC method combines low and high-fidelity models to reduce computation.
problem Inefficient computation of expensive target densities in scientific applications.
method Pseudo-marginal MCMC approach using a telescoping series of low-fidelity models.
result Asymptotically exact multi-fidelity MCMC algorithms for reduced computational cost.
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 combines autoencoder transfer learning with MSCP for accurate aerodynamic predictions.
problem Data scarcity in aerodynamic modeling limits the use of high-fidelity simulations.
method Autoencoder-based transfer learning with MSCP for uncertainty-aware data fusion.
result The model achieves high accuracy with minimal high-fidelity training data and robust uncertainty bands.
PASHA optimizes model tuning for large datasets with limited resources.
problem Expensive HPO and NAS for large datasets.
method Dynamic resource allocation approach.
result Significantly reduces computational resources while maintaining performance.
Unified framework for efficient surrogate modeling in manufacturing.
problem Large data requirements and heterogeneous data sources in manufacturing.
method Hierarchical multi-task multi-fidelity (H-MT-MF) framework for Gaussian process-based surrogate modeling.
result Improves prediction accuracy by up to 23% compared to existing methods.
We study the problem of black-box optimization of a noisy function in the presence of low-cost approximations or fidelities, which is motivated by problems like hyper-parameter tuning. In hyper-parameter tuning evaluating the black-box function at a point involves training a learning algorithm on a large data-set at a …
Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design. Recently, \emph{multi-fidelity} methods have garnered considerable attention since function evaluations have become increasingly expensive in such appli…
In many scientific and engineering applications, we are tasked with the maximisation of an expensive to evaluate black box function f. Traditional settings for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the exp…
Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification. Adaptive sampling for multi-fidelity Gaussian process is a changing task due to the fact that not only we seek to estimate the next samplin…
Optimal multi-fidelity best-arm identification reduces cost with better accuracy.
problem Finding the best arm with highest mean reward at minimum cost.
method Gradient-based approach with asymptotically optimal cost complexity.
result Asymptotically optimal cost complexity compared to existing methods.
Computational simulations with different fidelity have been widely used in engineering design. A high-fidelity (HF) model is generally more accurate but also more time-consuming than an low-fidelity (LF) model. To take advantages of both HF and LF models, multi-fidelity surrogate models that aim to integrate informatio…
PCTS optimizes noisy, delayed, multi-fidelity feedbacks in black-box optimization.
problem Optimizing unknown functions with noisy, delayed, and multi-fidelity feedbacks.
method ProCrastinated Tree Search (PCTS) with DUCB1 and DUCBV algorithms.
result PCTS achieves better regret bounds for delayed, noisy, and multi-fidelity feedbacks.