Results 101 to 110 of about 2,048 (231)

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

High-Order Integral Equation Methods for Diffraction Problems Involving Screens and Apertures [PDF]

, 2012
This thesis presents a novel approach for the numerical solution of problems of diffraction by infinitely thin screens and apertures. The new methodology relies on combination of weighted versions of the classical operators associated with the Dirichlet ...
Lintner, Stéphane Karl
core   +1 more source

Jacobian-AIME: A Local and Functional Approach to Interpreting Nonlinear Maps for Physics-Informed Neural Networks

IEEE Access
We propose Jacobian-AIME, a novel explanation framework for Physics-Informed Neural Networks (PINNs). PINNs map coordinates to physical fields governed by PDEs.
Kosuke Yano, Takafumi Nakanishi, Soichiro Ikuno   +2 more
doaj   +1 more source

Neural network augmented inverse problems for PDEs

, 2017
In this paper we show how to augment classical methods for inverse problems with artificial neural networks. The neural network acts as a prior for the coefficient to be estimated from noisy data. Neural networks are global, smooth function approximators and as such they do not require explicit regularization of the error functional to recover smooth ...
Berg, Jens, Nyström, Kaj
openaire   +2 more sources

Local Polynomial Regression and Filtering for a Versatile Mesh‐Free PDE Solver

International Journal for Numerical Methods in Fluids, Volume 98, Issue 7, Page 804-839, July 2026.
A high‐order, mesh‐free finite difference method for solving differential equations is presented. Both derivative approximation and scheme stabilisation is carried out by parametric or non‐parametric local polynomial regression, making the resulting numerical method accurate, simple and versatile. Numerous numerical benchmark tests are investigated for
Alberto M. Gambaruto
wiley   +1 more source

Stable Numerical Solution of an Elliptic PDE Inverse Problem Subject to Incomplete Boundary Conditions

Wasit Journal of Computer and Mathematics Science
This paper addresses the inverse problem of reconstructing complete steady-state solutions for elliptic partial differential equations when boundary information is incomplete a situation common in electromagnetic, thermal, and geophysical modeling where ...
ABBAS ALDNADOI
doaj   +1 more source

Multigrid Algorithms for Inverse Problems with Linear Parabolic PDE Constraints

SIAM Journal on Scientific Computing, 2008
We present a multigrid algorithm for the solution of source identification inverse problems constrained by variable-coefficient linear parabolic partial differential equations. We consider problems in which the inversion variable is a function of space only. We consider the case of $L^2$ Tikhonov regularization. The convergence rate of our algorithm is
Adavani, Santi S, Biros, George
openaire   +3 more sources

Stability of a Fully Discrete Local Discontinuous Galerkin Method for the Generalized Benjamin–Ono Equation

Numerical Methods for Partial Differential Equations, Volume 42, Issue 4, July 2026.
ABSTRACT The main purpose of this paper is to design a fully discrete local discontinuous Galerkin (LDG) scheme for the generalized Benjamin–Ono equation. First, we prove the L2$$ {L}^2 $$‐stability for the proposed semi‐discrete LDG scheme and obtained a suboptimal order of convergence for power nonlinear flux.
Mukul Dwivedi, Tanmay Sarkar
wiley   +1 more source

Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions [PDF]

, 2011
The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm which makes local moves by incorporating information about the gradient of the target density. In this paper we study the efficiency of MALA on a natural class of target measures
Thiéry, Alexandre H., Stuart, Andrew M., Pillai, Natesh S., Stuart, A. M.   +3 more
core   +1 more source

Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

International Journal of Geo-Engineering
The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions.
Yuan Feng, Jongwan Eun, Seunghee Kim, Yong-Rak Kim   +3 more
doaj   +1 more source