Results 131 to 140 of about 2,057 (240)

Neural‐Initialized Newton: Accelerating Nonlinear Finite Elements via Operator Learning

International Journal for Numerical Methods in Engineering, Volume 127, Issue 14, 30 July 2026.
ABSTRACT We propose a Newton‐based scheme, initialized by neural operator predictions, to accelerate the parametric solution of nonlinear problems in computational solid mechanics. First, a physics‐informed neural operator based on conditional neural fields or Fourier neural operators is trained to approximate the nonlinear parametric solution of the ...
Kianoosh Taghikhani, Yusuke Yamazaki, Jerry Paul Varghese, Markus Apel, Reza Najian Asl, Shahed Rezaei   +5 more
wiley   +1 more source

Reduced models for optimal control, shape optimization and inverse problems in haemodynamics [PDF]

, 2013
ECCOMAS PhD AwardInternational audienceDespite the computer resources nowadays available, it is still difficult - and often impossible - to deal with applications and scenarios involving the repeated solution of PDEs on different data settings (many-query
Manzoni, Andrea
core  

Physics-Informed Neural Network solutions for nonlinear wave equations: Applications to Rosenau–Hyman and Sharma–Tasso–Olver equations

Scientific African
This study investigates the application of Physics-Informed Neural Networks (PINNs) to nonlinear dispersive wave equations, focusing on the Rosenau–Hyman (RH) and Sharma–Tasso–Olver (STO) models.
Waleed Adel, Yassine Sabbar, Kottakkaran Sooppy Nisar   +2 more
doaj   +1 more source

Advancing Aquifer Characterization Through the Integration of Satellite Geodesy, Geomechanics, and Bayesian Inference

Geophysical Research Letters, Volume 53, Issue 14, 28 July 2026.
Abstract Unsustainable rates of groundwater (GW) depletion make GW management a priority. Effective GW management is hindered by the uncertainty in the predictions of aquifer models, but the increase of geodetic surface deformation data can improve aquifer characterization.
Amal Alghamdi, Marc A. Hesse, Jingyi Chen, Omar Ghattas   +3 more
wiley   +1 more source

Inverse Problems Using Reduced Basis Method [PDF]

, 2014
Inverse Problems is a field of great interest for many applications, such as parameter identification and image reconstruction. The underlying models of inverse problems in many applications often involve Partial Differential Equations (PDEs).
Gralla, Phil
core   +1 more source

Mechanistic‐Statistical Inference of Mosquito Dynamics From Mark‐Release‐Recapture Data

Population Ecology, Volume 68, Issue 3, July 2026.
Mark‐release‐recapture data contain valuable information on mosquito dispersal and survival, but this information is only indirectly observed through trap counts. We combine a mechanistic diffusion model with a statistical observation model to infer movement, mortality, and trap efficiency jointly.
Nga Nguyen, Olivier Bonnefon, René Gato, Luis Almeida, Lionel Roques   +4 more
wiley   +1 more source

Using Physics-Informed Neural Networks for Solving 2^nd-Order Volterra Integro-Differential Equation by DeepXDE Library

مجلة بغداد للعلوم
Many disciplines widely use deep learning (DL) as a key tool for investigating the behavior of various systems. Deep learning (DL) has recently been utilized to solve differential equations using physics-based input.
Oday Ahmed Jasim, Abdulghafor M. Al-Rozbayani   +1 more
doaj   +1 more source

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

CoRR
Inverse problems involving partial differential equations (PDEs) can be seen as discovering a mapping from measurement data to unknown quantities, often framed within an operator learning approach. However, existing methods typically rely on large amounts of labeled training data, which is impractical for most real-world applications.
Sung Woong Cho, Hwijae Son
openaire   +3 more sources

Data‐Efficient Electromagnetic Surrogate Solver Through Dissipative Relaxation Transfer Learning

Advanced Optical Materials, Volume 14, Issue 25, 3 July 2026.
Dissipative relaxation transfer learning (DIRTL) enables data‐efficient training of electromagnetic surrogate solvers by pretraining data generated with artificial material loss before fine‐tuning on target lossless data. The framework suppresses resonant outlier effects during early training, allowing effective adaptation to high‐amplitude resonances ...
Sunghyun Nam, Chan Y. Park, Min Seok Jang   +2 more
wiley   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, Volume 72, Issue 7, July 2026.
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