surrogate-model 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
A curated list of 100+ AI-ready tools for Computer-Aided Engineering, ranked by an AI-Readiness Score (agent-callability: MCP, Python API, CLI, pip). CFD, FEA, SPH, DEM, differentiable simulation, neural operators, PINNs, MCP servers.
[ESWA] NeuroXAI: Adaptive, Robust, Explainable Surrogate Framework for Determination of Channel Importance in EEG Application
A Keysight ADS/RFPro-based workflow for RFIC pixel-layout generation, EM simulation automation, HDF5 dataset creation, and CNN-based surrogate modeling.
An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.
WinDiNet: Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
Signal Filtering and Generation of Synthetic Time-Series.
Claude Code 代理技能，连接 Abaqus FEA 和机器学习替代：LHS 批处理数据集生成 + ODB 到机器学习准备好网格的 CSV。
A core library for engineering surrogate modeling and operator learning
Source code for Surrogate Modeling of Melt Pool Temperature Field using Deep Learning
NVIDIA PhysicsNeMo 中文教程书 · 8 章 + 4 附录 · PINN 与神经算子三种实现（裸 PyTorch / SDK / GPU production）
Fast, memory-efficient exact Gaussian processes in C++ and Python
[TIFS 2025] MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks
Curiosity → Perseverance rover wheel skin: Ansys FEA, analytical failure analysis, and a PCA–Gaussian-process surrogate model with a MATLAB GUI for instant stress/strain prediction and exports
An interactive Python dashboard for planning space missions. It features orbital maneuver calculations, animated 2D flight paths, and an AI model for predicting fuel needs, all wrapped in a clean, user-friendly interface.
Python package for Surrogate-Assisted Evolutionary Algorithms (SAEAs).
A workshop covering the calibration of agent-based models.
Autonomous engineering design agent: reason, design, GPU physics, optimize, learn. Evidence-graded engineering brain with external solver cross-validation. All results simulated, not physically validated.
Training code for CBAM-based PHVI classification and pitting-damage morphology regression associated with IJMS 2026.
Tesseract Hackathon Submission - Track 04.
Code and data for: Fast computational screening of extrusion parameters for bioprinting of skeletal myoblasts in alginate hydrogel