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Establishing Quantified Uncertainty in Neural Networks
Modular AI Trustworthy Engineering (MAITE) is a library of common types, protocols (a.k.a. structural subtypes), and utilities that can be used to support AI test and evaluation workflows.
Companion web application to equine with interactive UQ visualizations
📘 Automatic documentation from sources, for MkDocs.
An object-oriented component library supporting radiation transport applications.
MagNav: airborne Magnetic anomaly Navigation
A differentiable PDE solving framework for machine learning
Welcome to the Physics-based Deep Learning Book v0.3 - the GenAI Edition
Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond
PyTorch-centric library for evaluating and enhancing the robustness of AI technologies
Create powerful Hydra applications without the yaml files and boilerplate code.
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable i…
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning a…