Results 21 to 30 of about 11,070 (191)

Computation of frequency responses for linear time-invariant PDEs on a compact interval

, 2013
We develop mathematical framework and computational tools for calculating frequency responses of linear time-invariant PDEs in which an independent spatial variable belongs to a compact interval.
Jovanović, Mihailo R., Lieu, Binh K.
core   +1 more source

Branching diffusion representation of semi-linear elliptic PDEs and estimation using Monte Carlo method [PDF]

, 2018
We study semi-linear elliptic PDEs with polynomial non-linearity and provide a probabilistic representation of their solution using branching diffusion processes.
Agarwal, Ankush, Claisse, Julien
core   +3 more sources

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, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

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, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

On Integrable Doebner-Goldin Equations

, 1995
We suggest a method for integrating sub-families of a family of nonlinear {\sc Schr\"odinger} equations proposed by {\sc H.-D.~Doebner} and {\sc G.A.~Goldin} in the 1+1 dimensional case which have exceptional {\sc Lie} symmetries.
Anderson R L, Burgers J M, Cole J D, Courant R, Doebner H-D, Doebner H-D, Doebner H-D, Doebner H-D, Doebner H-D, Feynman R P, Fushchych W I, Fushchych W I, Fushchych W I, Fushchych W I, Fushchych W I, Fushchych W I, Goldin G A, Goldin G A, Guerra F, Hopf E, John F, Kibble T W B, Lax P, Lücke W, Nattermann P, Nattermann P, Nattermann P, Olver P J, Ovsiannikov L V, P Nattermann, R Zhdanov, Sabatier P C, Smolin L, Smoller J   +33 more
core   +2 more sources

“It Is Much Safer to Be Sparse than Connected”: Safe Control of Robotic Swarm Density Dynamics with PDE Optimization with State Constraints

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

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

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, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source

Two‐Phase Nanofluid Flow in a Non‐Newtonian Model Past a Deformable Sheet With Magnetized Environmental Effects: Statistical Modeling and ANOVA Analysis

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, MD. Shamshuddin, Subhajit Panda, Umair Khan   +3 more
wiley   +1 more source

Thermal Conductance and Mass Transport of Brinkman‐Type Nanofluids Across Porous Plates: A Prabhakar‐Fractional Approach

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT The paper establishes an advanced computing algorithm to investigate the thermosolutal dynamics of an electrically conductive Brinkman‐type nanofluid that moves in a porous channel, and the fluid is acted on by an inclined magnetic field exerted externally.
Urwa Shehbaz, Umair Khan, MD. Shamshuddin, Nermeen Abdullah, Samia Elattar   +4 more
wiley   +1 more source