Results 51 to 60 of about 35,012 (233)

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

Causality-Aware Training of Physics-Informed Neural Networks for Solving Inverse Problems

Mathematics
Inverse Physics-Informed Neural Networks (inverse PINNs) offer a robust framework for solving inverse problems governed by partial differential equations (PDEs), particularly in scenarios with limited or noisy data. However, conventional inverse PINNs do
Jaeseung Kim, Hwijae Son
doaj   +1 more source

Inverse Scattering Problem for Vector Fields and the Cauchy Problem for the Heavenly Equation

, 2006
We solve the inverse scattering problem for multidimensional vector fields and we use this result to construct the formal solution of the Cauchy problem for the second heavenly equation of Plebanski, a scalar nonlinear partial differential equation in ...
Ablowitz, Bogdanov, Bogdanov, Bogdanov, Chakravarty, Courant, Deans, Dunajski, Dunajski, Dunajski, Manakov, Manakov, Manakov, Manas, Neyzi, P.M. Santini, Plebanski, S.V. Manakov, Yang, Zakharov, Zakharov   +20 more
core   +1 more source

“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

Physics-informed linear model (PILM): analytical representations and application to crustal strain rate estimation

Earth, Planets and Space
Many physical systems are described by partial differential equations (PDEs), and solving these equations and estimating their coefficients or boundary conditions (BCs) from observational data play a crucial role in understanding the associated phenomena.
Tomohisa Okazaki
doaj   +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

Collage-type approach to inverse problems for elliptic PDEs on perforated domains

Electronic Journal of Differential Equations, 2015
We present a collage-based method for solving inverse problems for elliptic partial differential equations on a perforated domain. The main results of this paper establish a link between the solution of an inverse problem on a perforated domain and ...
Herb E. Kunze, Davide La Torre
doaj  

Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design

, 2019
An optimal experimental set-up maximizes the value of data for statistical inferences and predictions. The efficiency of strategies for finding optimal experimental set-ups is particularly important for experiments that are time-consuming or expensive to
Beck, Joakim, Dia, Ben Mansour, Espath, Luis F. R., Tempone, Raul   +3 more
core   +1 more source