A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
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Updated
May 14, 2026 - Python
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays
surrogate quantitative interpretability for deepnets
UQPyL is a python package for uncertainty quantification and parameter optimization. 参数不确定性分析及优化工具包。
Blackbox provides a standarized and efficient interface for evaluating variety of blackbox functions for various surrogate-based design activities
cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics (JCOMP 2024)
Mitigating the high computational costs associated with applying Bayesian model updating in inverse problems / Uncertainty Quantification and Efficient Sensitivity Analysis by using Surrogate Models
Preliminary investigation on transferring the GeoPT checkpoint to heat-sink solid-conduction surrogate modeling
squid repository for manuscript analysis
3D physics-based AI surrogate reservoir model for fast simulation, forecasting and many-query analysis in gas and gas-condensate reservoirs.
Tools for constructing a surrogate model for a stochastic numerical model using Probabilistic Learning on Manifolds in a small data context
Multi-Criteria Decision Making (MCDM) Framework for Building Energy Systems with Expedited Computation using Machine Learning (ML) Techniques
Generative Surrogate Modelling for battery electrode microstructures using Conditional 3D Diffusion. Built for Isambard-AI (GH200) to predict mechanical deformation.
Hierarchical generative and regressive machine learning for next generation materials screening
Statistical learning models library for blackbox optimization
Trust-Region Filter/Funnel (TRF) solver is developed using the concepts from nonlinear optimisation, derivative-free optimisation and surrogate modelling, and is used to optimise grey box optimisation problems (coupling glass box mathematical models with available derivative information and the black box models without derivative information).
Learning Aerodynamics Through Data to Improve Optimization Algorithms
This repository contains scripts that were used for the experiments of our work named "Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling".
Surrogate modelling technique selectors
Deep learning models for generating stress fields in microstructures.
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