Perspective: Machine learning in experimental solid mechanics

NR Brodnik, C Muir, N Tulshibagwale, J Rossin… - Journal of the …, 2023 - Elsevier
Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

A perspective on applied geochemistry in porous media: Reactive transport modeling of geochemical dynamics and the interplay with flow phenomena and physical …

H Deng, M Gharasoo, L Zhang, Z Dai, A Hajizadeh… - Applied …, 2022 - Elsevier
In many practical geochemical systems that are at the center of providing indispensable
energy, resources and service to our society,(bio) geochemical reactions are coupled with …

[HTML][HTML] Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

S Perez, S Maddu, IF Sbalzarini, P Poncet - Journal of Computational …, 2023 - Elsevier
In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian
Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it …

Capturing the dynamic processes of porosity clogging

MI Lönartz, Y Yang, G Deissmann… - Water resources …, 2023 - Wiley Online Library
Understanding mineral precipitation induced porosity clogging and being able to quantify its
non‐linear feedback on transport properties is fundamental for predicting the long‐term …

[HTML][HTML] Inverse Physics-Informed Neural Networks for transport models in porous materials

M Berardi, FV Difonzo, M Icardi - Computer Methods in Applied Mechanics …, 2025 - Elsevier
Abstract Physics-Informed Neural Networks (PINN) are a machine learning tool that can be
used to solve direct and inverse problems related to models described by Partial Differential …

Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution

S Perez, P Poncet - Computational Geosciences, 2024 - Springer
In this article, we present a novel data assimilation strategy in pore-scale imaging and
demonstrate that this makes it possible to robustly address reactive inverse problems …

Physics‐informed neural networks trained with time‐lapse geo‐electrical tomograms to estimate water saturation, permeability and petrophysical relations at …

C Sakar, N Schwartz, Z Moreno - Water Resources Research, 2024 - Wiley Online Library
Determining soil hydraulic properties is complex, posing ongoing challenges in managing
subsurface and agricultural practices. Electrical resistivity tomography (ERT) is an appealing …

Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions

A Chakraborty, A Rabinovich, Z Moreno - Advances in Water Resources, 2024 - Elsevier
Multi-phase flow simulations in heterogeneous porous media are essential in many
applications, for example, CO 2 sequestration, enhanced oil and gas recovery, groundwater …

Displacement-driven approach to nonlocal elasticity

W Ding, S Patnaik, S Sidhardh, F Semperlotti - … of Structures and Materials, 2024 - Elsevier
Nonlocal interactions are intrinsic to multiscale heterogeneous solids. In the most general
case, nonlocal interactions exhibit a position-dependent strength whose spatial distribution …

AI-driven inverse design of materials: Past, present and future

XQ Han, XD Wang, MY Xu, Z Feng, BW Yao… - arXiv preprint arXiv …, 2024 - arxiv.org
The discovery of advanced materials is the cornerstone of human technological
development and progress. The structures of materials and their corresponding properties …