PoreML is an open‑source, GPU‑native framework that unifies data generation, model training, and evaluation for multiphase flow in porous media. It includes a validated lattice Boltzmann solver, a 3.3 TB dataset of 560 simulation runs with 158,546 time steps covering synthetic and micro‑CT derived structures, and a learning framework that assesses one‑step prediction and autoregressive rollouts with domain‑specific protocols. Five diverse models were evaluated, and two challenges test transfer to larger domains and to real‑material geometries.
arXiv:2607. 24274v1 Announce Type: cross Abstract: Inverse design of three-dimensional porous media is central to applications in filtration, catalysis, energy storage, fuel cells, thermal management, and biomedical scaffolds, but remains challenging because many distinct pore geometries can share similar porosity or permeability while small structural changes can strongly affect transport behaviour.
The paper introduces a physics-informed neural network (PINN) framework for modeling fluid flow in dual‑network porous media, specifically addressing double porosity/permeability (DPP) systems. The framework embeds governing equations and boundary conditions into the loss function with adaptive weighting, employs dynamic collocation point selection, and uses shared trunk architectures to efficiently capture coupled pore‑network behavior. It is mesh‑free, accurately handles discontinuities across layered domains, and supports robust inverse analysis for parameter identification, with a systematic convergence study validating its stability and accuracy.
arXiv:2602. 06989v2 Announce Type: replace-cross Abstract: Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, and underground hydrogen storage.
arXiv:2603. 11250v2 Announce Type: replace-cross Abstract: Accurate modeling of gas flow through porous media is critical for many technological applications, including reservoir performance prediction, carbon capture and sequestration, and fuel cells and batteries.
arXiv:2602. 22188v2 Announce Type: replace Abstract: Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces.
arXiv:2607. 19198v1 Announce Type: cross Abstract: Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample.
The paper introduces the Constitutive Parameterized Deep Energy Method (CPDEM), a physics‑driven neural network that embeds stochastic material parameters directly into the strain energy density functional. By training via expected energy minimization over the parameter domain, CPDEM learns a continuous solution manifold, enabling zero‑shot, real‑time inference of displacement fields for any material parameters without retraining or dataset generation. The method is validated on linear elasticity, finite‑strain hyperelasticity, and highly nonlinear contact mechanics, marking the first purely physics‑driven deep learning approach that efficiently handles continuous multi‑parameter variations in solid mechanics.
arXiv:2607. 22280v2 Announce Type: replace-cross Abstract: Compressible multiphase flows involving shocks and material interfaces arise in applications such as bubble collapse and droplet breakup, where strong nonlinear interactions produce complex interface deformation, mixing, and multiscale dynamics.
arXiv:2609.36504v1 Announce Type: new Abstract: Accurate prediction of three-dimensional (3D) microstructure evolution remains computationally demanding because high-fidelity phase-field simulations...
FlowMesh is a generative framework that produces full-field thermal‑fluid realizations directly on fixed unstructured meshes by combining permutation‑invariant mesh encoding, deterministic latent flow matching, and an l1 penalty on transport velocity. It achieves high probability‑density‑function coverage (91.7%–97.0%) and significant reductions in temperature‑field Kullback–Leibler divergence compared to latent diffusion models, while generating each realization in about 1.2 s—over 500‑fold faster than traditional computational fluid dynamics. The method enables large‑scale, data‑driven characterization of thermal‑fluid states on complex geometries without the need for grid interpolation or stochastic denoising chains.
The paper introduces CG-OMatG, an equivariant Riemannian flow-based generative model that predicts molecular crystal structures using a coarse-grained, hierarchical representation. It treats molecules as rigid bodies, performs inter- and intra-molecular message passing, and learns to reconstruct molecule centroids, orientations, and lattice parameters conditioned on chemical species and conformer geometry. The model is trained on OMC25 and CSD datasets, fine-tuned with policy gradient reinforcement learning to favor low-energy structures, and validated against a CSP blind test benchmark using COMPACK packing-similarity analysis.
arXiv:2605. 17581v2 Announce Type: replace-cross Abstract: Flow in porous media is difficult to address using standard analytical or numerical methods due to its complexity.