arXiv Statistics ML By Nicholas Wright, Oliver Maclaren, Piaras Kelly, Suresh Advani, Ruanui Nicholson

Online Gate-Driven Flow Control in Resin Transfer Moulding Using a Neural-Network Surrogate

Read the original on arXiv Statistics ML →

The Flow has not summarised this story yet — read it at arXiv Statistics ML.

arXiv Machine Learning
Jun 10

PL-KKT-hPINN: Enforcing Nonlinear Equality Constraints on Neural Networks via Piecewise-Linear Projection

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.

By Fateme Mohammad Mohammadi, Hector Budman, Joshua L. Pulsipher
arXiv Machine Learning
Sep 4

Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data

The paper presents a differentiable hybrid modelling framework that combines a JAX finite volume population balance solver with learnable neural network components. This approach learns constitutive laws and initial conditions directly from experimental data, improving the fidelity of transport models in chemical engineering. The framework’s differentiability also enables optimisation of experimental settings for desired process outcomes.

By Arthur Jessop, Mohammed Alsubeihi, Ben Moseley, Ashwin Kumar Rajagopalan
arXiv Machine Learning
Aug 28

COFM: Consistent Optimal Transport Flow Matching via Partially Input Convex Neural Networks

The paper introduces COFM, a framework for consistent optimal transport flow matching that uses partially input convex neural networks (PICNN) to parameterize the transport potential. By adding a Hamilton‑Jacobi residual to the training objective, COFM enforces dynamical consistency and supports both one‑step transport and multi‑step ODE sampling without costly inner optimization. Experiments on benchmark datasets show that COFM achieves competitive performance while reducing L^2‑UVP by over 2× and cutting computational time by about 9× compared to state‑of‑the‑art models.

By Fanghui Song, Zhongjian Wang, Jiebao Sun
Hugging Face Trending Papers
Sep 3

Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data

The paper presents a differentiable hybrid modelling framework that combines a JAX finite volume population balance solver with neural network components to learn constitutive laws and initial conditions directly from experimental data. This approach addresses biases from hand‑picked models and the limitations of black‑box surrogates, enabling more accurate transport predictions. The framework’s differentiability also facilitates optimisation of experimental settings for desired process outcomes.