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A package for the sparse identification of nonlinear dynamical systems from data
physics-informed neural network for elastodynamics problem
Filter visualization, Feature map visualization, Guided Backprop, GradCAM, Guided-GradCAM, Deep Dream
Application of Graph Neural Networks to predict material properties from their microstructures.
Graph neural networks for stress and strain fields prediction
The processed dataset for paper "redicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks"
Codes for translating structural defects to atomic properties
Using Fourier Neural Operator to measure the physical fields for a mode-I failure of 2D composites
A Python implementation of global optimization with gaussian processes.
GAN/convolutional and Transformer models to predict missing mechanical information given limited known data in part of the domain, and further characterize the composite geometries from the recover…
This repository has the best trained ML model used in the paper entitled "Fatigue damage reconstruction in glass/epoxy composites via thermal analysis and machine learning: a theoretical study", by…
Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurements".
ADA-F (Anti-Derivatives Approximator from Fourier series expansion)
A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold N…
Kolmogorov-Arnold Networks (KAN) using orthogonal polynomials instead of B-splines.
Repo for Li, Kafka, Gao et al 2019 "Clustering discretization methods for generation of material performance databases in machine learning and design optimization"
Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Networks (PINNs) and reduced basis methods (RBM) to the non- linear…
Physics-informed learning of governing equations from scarce data