Implementations of three neural operators and application in Bayesian inference problems
This project develops neural-network surrogate models for fast, accurate European option pricing under stochastic volatility frameworks. Traditional methods like Monte Carlo simulations are computationally expensive. The goal is to use machine learning models, trained on synthetic data, for orders-of-magnitude faster pricing with similar accuracy.
Tatyana V2 — a Residual MLP neural surrogate for TGLF-like linear stability analysis.
JAX/Flax physics-informed neural network with jax2tf export — benchmark JAX vs PyTorch vs TensorFlow
Developing methods to certify that AI used for EIT preserves clinically meaningful information about lung recruitment and ventilation, not just accurate voltage predictions. Copyright 2026 Katherine J. Ombrellaro
A development of metabolic neural networks for microorganisms
Add a description, image, and links to the neural-surrogate topic page so that developers can more easily learn about it.
To associate your repository with the neural-surrogate topic, visit your repo's landing page and select "manage topics."