Results 41 to 50 of about 1,345 (197)

Families of Orbits Produced by Three-Dimensional Central and Polynomial Potentials: An Application to the 3D Harmonic Oscillator

Axioms, 2023
We study three-dimensional potentials of the form V=U(xp+yp+zp), where U is an arbitrary function of C2-class, and p∈Z, which produces a preassigned two-parametric family of spatial regular orbits given in the solved form f(x,y,z) = c1, g(x,y,z) = c2 (c1,
Thomas Kotoulas
doaj   +1 more source

The Numerical Solution of Singularly Perturbed Nonlinear Partial Differential Equations in Three Space Variables: The Adaptive Explicit Inverse Preconditioning Approach

Modelling and Simulation in Engineering, 2019
Critical comments on the complexity of computational systems and the basic singularly perturbed (SP) concepts are given. A class of several complex SP nonlinear elliptic equations arising in various branches of science, technology, and engineering is ...
Anastasia-Dimitra Lipitakis
doaj   +1 more source

On numerical simulation of liquid polymer moulding

Mathematical Modelling and Analysis, 2003
In this paper we consider numerical algorithms for solving the system of nonlinear PDEs, arising in modeling of liquid polymer injection. We investigate the particular case when a porous preform is located within the mould, so that the liquid polymer is ...
R. Čiegis, O. Iliev
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

Proximity Ferroelectricity in Compositionally Graded Structures

Advanced Electronic Materials, EarlyView.
We perform the finite element modeling of the polarization switching in the compositionally graded AlN‐Al1‐xScxN and ZnO‐Zn1‐xMgxO structures and reveal the switching of spontaneous polarization in the whole structure in all these systems. The coercive field to switch is significantly lower than the electric breakdown field of the unswitchable AlN and ...
Eugene A. Eliseev, Anna N. Morozovska, Sergei V. Kalinin, Long‐Qing Chen, Venkatraman Gopalan   +4 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

Transient modeling of extraction columns: Parameter estimation, uncertainty analysis, and operation optimization

AIChE Journal, EarlyView.
Abstract Despite extensive modeling efforts in extraction research, transient column models are rarely applied in industry due to concerns regarding parameter identifiability and model reliability. To address this, we analyzed uncertainty propagation from estimated parameters in a previously introduced column model and assessed identifiability via ill ...
Andreas Palmtag, Jannik Dohmen, Andreas Jupke   +2 more
wiley   +1 more source

A penalty method for PDE-constrained optimization in inverse problems [PDF]

Inverse Problems, 2015
Many inverse and parameter estimation problems can be written as PDE-constrained optimization problems. The goal, then, is to infer the parameters, typically coefficients of the PDE, from partial measurements of the solutions of the PDE for several right-hand-sides.
van Leeuwen, T., Herrmann, Felix J.
openaire   +5 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