Results 41 to 50 of about 1,940 (229)

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +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

Toward Intelligent Multimodal Holography for Real‐Time Chemical Imaging of Dynamic Ion Separation

Advanced Intelligent Discovery, EarlyView.
Intelligent multimodal holography integrates digital off‐axis holography, spectroscopic imaging, and AI‐driven reconstruction to visualize ion transport and chemical dynamics in real time. In this perspective paper, we outline how this approach enables label‐free, chemically specific monitoring of complex environments and discuss its potential to ...
Giovanna Ricchiuti, Sarah I. Allec, Maxim Ziatdinov, Venkateshkumar Prabhakaran   +3 more
wiley   +1 more source

GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs

, 2023
Physics-Informed Neural Network (PINN) has proven itself a powerful tool to obtain the numerical solutions of nonlinear partial differential equations (PDEs) leveraging the expressivity of deep neural networks and the computing power of modern ...
Koohy, Shawn, Chen, Yanlai
core  

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +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

Moments and convex optimization for analysis and control of nonlinear partial differential equations

, 2022
This work presents a convex-optimization-based framework for analysis and control of nonlinear partial differential equations. The approach uses a particular weak embedding of the nonlinear PDE, resulting in a linear equation in the space of Borel ...
Didier Henrion, Henrion, Didier, Lasserre, Jean-Bernard, Jean Bernard Lasserre, Korda, Milan, Milan Korda   +5 more
core   +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

Deep Learning of Delay-Compensated Backstepping for Reaction-Diffusion PDEs

, 2023
Deep neural networks that approximate nonlinear function-to-function mappings, i.e., operators, which are called DeepONet, have been demonstrated in recent articles to be capable of encoding entire PDE control methodologies, such as backstepping, so that,
Krstić, Miroslav, Wang, Shanshan, Diagne, Mamadou   +2 more
core  

Radiation‐Absorptive Heat Transport in Buoyancy‐Driven MHD Nanofluids Flow With Cross‐Diffusion and Chemical Interaction Effects Over a Vertical Moving Plate

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT This article investigates the Soret–Dufour cross‐diffusion effects on radiation‐absorptive unsteady free‐convection of magnetized nanofluids (TiO2–water$$ {\mathrm{TiO}}_2\hbox{--} \mathrm{water} $$ and Cu–water$$ \mathrm{Cu}\hbox{--} \mathrm{water} $$) flow over a vertical moving permeable plate.
B. Prabhakar Reddy, Jumanne Mng'ang'a, P. V. Kanaka Rao   +2 more
wiley   +1 more source