3 papers
stat.ML2026
A Hybrid Conditional Diffusion-DeepONet Framework for High-Fidelity Stress Prediction in Hyperelastic Materials
Purna Vindhya Kota, Meer Mehran Rashid, Somdatta Goswami +1
Predicting stress fields in hyperelastic materials with complex microstructures remains challenging for traditional deep learning surrogates, which struggle to capture both sharp s…
cs.CE2026
Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
George D. Pasparakis, Himanshu Sharma, Rushik Desai +4
A physics-constrained Gaussian Process regression framework is developed for predicting shocked material states and their associated uncertainties along the Hugoniot curve using da…
stat.ML2024
Bayesian neural networks for predicting uncertainty in full-field material response
George D. Pasparakis, Lori Graham-Brady, Michael D. Shields
Stress and material deformation field predictions are among the most important tasks in computational mechanics. These predictions are typically made by solving the governing equat…