Results 31 to 40 of about 33,202 (148)
Hybrid PDE solver for data-driven problems and modern branching
, 2017 The numerical solution of large-scale PDEs, such as those occurring in data-driven applications, unavoidably require powerful parallel computers and tailored parallel algorithms to make the best possible use of them.
Brown, André EX +11 more
core +3 more sources
Advanced Science, EarlyView.We introduce AutomataGPT, a generative pretrained transformer (GPT) trained on synthetic spatiotemporal data from 2D cellular automata to learn symbolic rules. Demonstrating strong performance on both forward and inverse tasks, AutomataGPT establishes a scalable, domain‐agnostic framework for interpretable modeling, paving the way for future ...
Jaime A. Berkovich +2 more
wiley +1 more source
Efficient Gluing of Numerical Continuation and a Multiple Solution Method for Elliptic PDEs
, 2014 Numerical continuation calculations for ordinary differential equations (ODEs) are, by now, an established tool for bifurcation analysis in dynamical systems theory as well as across almost all natural and engineering sciences. Although several excellent
Kuehn, Christian
core +1 more source
AI in chemical engineering: From promise to practice
AIChE Journal, EarlyView.Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Nonlinear elliptic equations with high order singularities [PDF]
, 2016 We study non-variational degenerate elliptic equations with high order singular structures. No boundary data are imposed and singularities occur along an {\it a priori} unknown interior region. We prove that positive solutions have a universal modulus of
Teixeira, Eduardo V.
core
Advanced Intelligent Discovery, EarlyView.We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.This paper proposes a novel control framework to ensure safety of a robotic swarm. A feedback optimization controller is capable of driving the swarm toward a target density while keeping risk‐zone exposure below a safety threshold. Theory and experiments show how safety is more effectively achieved for sparsely connected swarms.
Longchen Niu, Gennaro Notomista
wiley +1 more source
Advanced Intelligent Systems, EarlyView.Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez +4 more
wiley +1 more source
Asia-Pacific Journal of Chemical Engineering, EarlyView.ABSTRACT This paper presents a comprehensive numerical analysis of magnetohydrodynamic (MHD) Casson nanofluid movement over a permeable, linearly stretching sheet, integrating the contributions of non‐uniform heat generation or absorption and chemical interaction.
Manoj Kumar Sahoo +3 more
wiley +1 more source
External optimal control of fractional parabolic PDEs
, 2019 In this paper we introduce a new notion of optimal control, or source identification in inverse, problems with fractional parabolic PDEs as constraints. This new notion allows a source/control placement outside the domain where the PDE is fulfilled.
Antil, Harbir +2 more
core +1 more source