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.
JAX/Flax physics-informed neural network with jax2tf export — benchmark JAX vs PyTorch vs TensorFlow
Tatyana V2 — a Residual MLP neural surrogate for TGLF-like linear stability analysis.
Bilingual web application for exploring validated one-dimensional reactive-transport simulations and comparing analytical, finite-volume, and neural solutions.
Certifies 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
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