Perspective: Machine learning in experimental solid mechanics
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 …
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 …
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 …
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
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 …
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 …
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
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 …
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 …
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 …
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
Multi-phase flow simulations in heterogeneous porous media are essential in many
applications, for example, CO 2 sequestration, enhanced oil and gas recovery, groundwater …
applications, for example, CO 2 sequestration, enhanced oil and gas recovery, groundwater …
Displacement-driven approach to nonlocal elasticity
Nonlocal interactions are intrinsic to multiscale heterogeneous solids. In the most general
case, nonlocal interactions exhibit a position-dependent strength whose spatial distribution …
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 …
development and progress. The structures of materials and their corresponding properties …