A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

Encoding physics to learn reaction-diffusion processes

[Neurips 2024] A benchmark suite for autoregressive neural emulation of PDEs. (≥46 PDEs in 1D, 2D, 3D; Differentiable Physics; Unrolled Training; Rollout Metrics)

A foundation model to learn multiple physical systems at once

3D CNN to predict single-phase flow velocity fields

A framework based on the tensor train decomposition for working with multivariate functions and multidimensional arrays

Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs

Dimension reduced surrogate construction for parametric PDE maps

A hydraulic surrogate model and real-time control methods of urban drainage networks.

Multi-fidelity Generative Deep Learning Turbulent Flows

[ESWA] NeuroXAI: Adaptive, Robust, Explainable Surrogate Framework for Determination of Channel Importance in EEG Application

In this repository I publish the python code, that was part of my master thesis. The thesis can be found here, however its in German though, sry. :/

AI-Powered Micro Turbojet Engine Design System — Computational engineering for autonomous engine design

Code attached to the paper: Uncertainty Quantification of Surrogate Models using Conformal Prediction

Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions

Python package 'dgpsi' for deep and linked Gaussian process emulations

PyTorch implemention of the Position-induced Transformer for operator learning in partial differential equations

A toolbox for the calibration and evaluation of simulation models.

Physics-Informed Neural Network, Finite Element Method enhanced neural network, and FEM data-based neural network

Resource Utilization and Latency Estimation for ML on FPGA.