arXiv:2607. 19241v1 Announce Type: new Abstract: Real-fluid thermodynamic property evaluation is a major computational cost in supercritical combustion simulations.
arXiv:2607. 09582v1 Announce Type: cross Abstract: We present a physics-constrained machine learning framework for accelerating the direct numerical simulation (DNS) of turbulent reacting flows.
arXiv:2606. 14729v1 Announce Type: cross Abstract: Turbulent combustion simulations are crucial for many scientific and engineering systems.
arXiv:2606. 10682v1 Announce Type: new Abstract: While physics-informed neural networks (PINNs) have shown strong potential for process modeling, physical equations are only enforced as soft constraints during training, and thus, they do not guarantee constraint satisfaction at inference.
arXiv:2608. 00212v1 Announce Type: new Abstract: Spatial atomic layer deposition (SALD) is a leading atmospheric-pressure, high-throughput route to industrial ALD, but design and control are limited by the cost of predicting surface coverage: high-fidelity CFD is far too slow for operating-window scans, while analytic models miss transport modulation such as the gas curtain.
The paper introduces Conditional Flow Matching (CFM) for engineering inverse design, comparing it to conditional diffusion models and cGANs on EngiBench structural and thermal benchmarks. CFM outperforms the baselines in cumulative and final optimality gaps, mean volume‑fraction deviation, and throughput, achieving up to 66× faster sample generation with fewer network evaluations. The study demonstrates CFM’s effectiveness as a warm‑start generator for gradient‑based refinement in PDE‑constrained design problems.
arXiv:2609.25505v1 Announce Type: cross Abstract: Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics nu...
arXiv:2606. 27354v1 Announce Type: cross Abstract: Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical task: once trained, they struggle to correct their own constraint violations and extrapolate beyond the training distribution.
arXiv:2606. 04420v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weighted combination of residual, boundary, initial, and data losses.
arXiv:2511. 05879v5 Announce Type: replace-cross Abstract: Hydrogen crossover is a critical safety and efficiency constraint in high-pressure polymer electrolyte membrane water electrolysis (PEMWE), but accurate prediction remains difficult because data are limited, transport physics are strongly coupled, and industrial operation requires reliable extrapolation beyond observed conditions.
arXiv:2409. 05885v2 Announce Type: replace Abstract: Characterizing nonlinear flame response is critical for predicting thermoacoustic instabilities in propulsion combustors, yet obtaining a comprehensive response map through high-fidelity simulations remains computationally prohibitive.
arXiv:2606. 27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining.
arXiv:2606. 18175v1 Announce Type: cross Abstract: We present a numerical method for the forward solution of nonlinear partial differential equations (PDEs) in which Bellman-Kalaba quasilinearization reduces the nonlinear problem to a sequence of linear subproblems, each discretized by collocation onto a trial space that is linear in its parameters and solved by a single direct linear least-squares QR factorization.