The paper introduces a unified framework that combines adaptive surrogate modeling with simulation-driven inverse design for microdroplet generators. High‑fidelity Volume‑of‑Fluid simulations supply training data, while neural‑network ensembles predict flow regimes and droplet diameters; ensemble‑based uncertainty quantification identifies critical parameter regions near regime transitions. A physics‑informed enrichment strategy and Particle Swarm Optimization are used to refine the surrogates and perform constrained inverse design, achieving markedly higher prediction accuracy and reliable identification of physically realizable configurations.
Microfluidic droplet generation is critical for many applications, yet efficient device design in high-dimensional parameter spaces remains computationally prohibitive due to complex multiphase flow d...
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:2606. 09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achieving speedups of several orders of magnitude over traditional numerical solvers.
arXiv:2608. 16870v1 Announce Type: new Abstract: Accurate classification of circulating tumor cell (CTC) phenotypes can provide valuable information for assessing metastatic potential.
arXiv:2606. 09857v1 Announce Type: new Abstract: Reduced-order models (ROMs) provide an efficient surrogate for complex multiscale systems, but their predictive accuracy is often compromised by truncation errors and the inadequate representation of interactions between resolved and unresolved scales.
arXiv:2607. 18294v1 Announce Type: new Abstract: Machine learning surrogate models are increasingly being explored in engineering product development to augment simulation-driven design, offering near-instantaneous predictions that complement computationally expensive high-fidelity analyses.
The paper presents cross‑domain pretraining for neural surrogates in steady‑state computational fluid dynamics (CFD). By training models across varied geometries, boundary conditions, and fidelities, the authors show that such pretraining improves zero‑ and few‑shot performance compared to training from scratch or transferring from domain‑specific experts. Finetuning a pretrained cross‑domain model can reduce errors by 2–3× at the same sample size and require eight times fewer samples to reach the same error, with benefits that grow with model size and dataset diversity.
arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).
arXiv:2606. 24696v1 Announce Type: cross Abstract: Physics-informed surrogate models can accelerate computational fluid dynamics simulations.
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:2609.25430v1 Announce Type: new Abstract: Neural surrogates can substantially accelerate computer-aided engineering (CAE) workflows, but their use in design requires uncertainty estimates that...
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.