14 citations · 14 across the 6 of their papers we have counts for
6 papers
Sparse data limit what a mechanistic corrosion model can predict
Conrard Giresse Tetsassi Feugmo
Mechanistic corrosion models are routinely fitted with four to six parameters to a few measurements and extrapolated across service lifetimes, yet whether the data determine them i…
Differentiable inverse design of short-range order in high-entropy alloys: from target sro to target property
Tiancheng Ding, Conrard Giresse Tetsassi Feugmo
Short-range order (SRO) governs the mechanical response of multi-principal-element alloys, but designing an alloy for a target property usually means solving two disconnected probl…
A Systematic Benchmark of Physics-Informed Neural Network Architectures for the Stiff Poisson-Nernst-Planck System: Adaptive LossWeighting and Multi-Scale Resolution
David Pankaczy, Conrard Giresse Tetsassi Feugmo
The Poisson Nernst Planck PNP system constitutes a canonical stiff coupled PDE problem where the charge density prefactor produces extreme coefficient ratios and the electric doubl…
Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks
Conrard Giresse Tetsassi Feugmo, David Pankaczy
The Neural Spectral Element Method (NSEM) evaluates each network only at fixed Legendre-Gauss-Lobatto quadrature nodes and replaces all derivative calls with precomputed spectral d…
Automatic graph representation algorithm for heterogeneous catalysis
Zachary Gariepy, ZhiWen Chen, Isaac Tamblyn +2
One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of ML-aided exploration, m…
Neural evolution structure generation: High Entropy Alloys
Conrard Giresse Tetsassi Feugmo, Kevin Ryczko, Abu Anand +2
We propose a method of neural evolution structures (NESs) combining artificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate High Entropy Alloys (HEAs) struc…