[ESWA] NeuroXAI: Adaptive, Robust, Explainable Surrogate Framework for Determination of Channel Importance in EEG Application
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Updated
Dec 18, 2024 - Python
[ESWA] NeuroXAI: Adaptive, Robust, Explainable Surrogate Framework for Determination of Channel Importance in EEG Application
A curated list of 113 AI-ready tools for Computer-Aided Engineering — CFD, FEA, SPH, DEM, differentiable simulation, neural operators, PINNs, MCP servers. Python APIs, CLI, mesh generation, optimization.
An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.
Signal Filtering and Generation of Synthetic Time-Series.
Source code for Surrogate Modeling of Melt Pool Temperature Field using Deep Learning
A core library for engineering surrogate modeling and operator learning
WinDiNet: Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
A Keysight ADS/RFPro-based workflow for RFIC pixel-layout generation, EM simulation automation, HDF5 dataset creation, and CNN-based surrogate modeling.
Explainable AI for time series: gradient-based counterfactual generation for the non-differentiable Random Shapelet Forest, using PyTorch surrogate models (Feedforward, LSTM, CNN, Transformer) and a Streamlit UI. Research internship, DSV — Stockholm University.
This project builds a machine-learning surrogate model using data generated from a quasi-6-DoF physics simulator replacing numerical integration with a neural network that can predict full 3-D trajectories including altitude, downrange motion, and lateral deflection hundreds of times faster while maintaining meter-level accuracy.
neural network surrogate for CFD simulations
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.
A recurrent neural network surrogate model with few-shot learning strategy for CO2 storage in deep subsurface saline aquifer
Deep Residual Transformer Neural Network (DRTNN)
Supplementary materials for Siggraph 2020 technical paper Fabrication-in-the-Loop Co-Optimization of Surfaces and Styli for Drawing Haptics
Surrogate model for predicting free fall motion. The project uses machine learning to replicate the parabolic trajectory of free fall based on initial velocity and launch angle. Includes MSE-based performance evaluation and trajectory comparison plots.
Physics-aware neural surrogate for black hole accretion flow (GRMHD-like) using Fourier Neural Operators.
COMSOL multiphysics simulation of LPBF melt pool physics on Inconel 718, 2D coupled Marangoni convection, heat transfer, and thermo-mechanical stress; 3D single-track keyhole detection (3,452 K peak); Clausius-Clapeyron recoil pressure model; Random Forest ML surrogate (R2=0.977, 12M× speedup over COMSOL).
ML-accelerated co-design of mobility and power subsystems for lunar micro-rovers
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