Results 81 to 90 of about 2,057 (240)
EntropyPhysics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal ...
Yanbing Liu +3 more
doaj +1 more source
Feedback Linearisation with State Constraints
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT Feedback Linearisation (FBL) is a widely used technique that applies feedback laws to transform input‐affine nonlinear control systems into linear control systems, allowing for the use of linear controller design methods such as pole placement.
Songlin Jin, Yuanbo Nie, Morgan Jones
wiley +1 more source
Direct and inverse source problems for degenerate parabolic equations [PDF]
, 2020 Degenerate parabolic partial differential equations (PDEs) with vanishing or unbounded leading coefficient make the PDE non-uniformly parabolic, and new theories need to be developed in the context of practical applications of such rather unstudied ...
Kostin, AB +3 more
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An analysis of constraint-relaxation in PDE-based inverse problems
Inverse ProblemsAbstract Many inverse problems are naturally formulated as a PDE-constrained optimization problem. These non-linear, large-scale, constrained optimization problems know many challenges, of which the inherent non-linearity of the problem is an important one.
T. van Leeuwen (Tristan) +1 more
openaire +2 more sources
Artificial Intelligence for Engineering, EarlyView.Caputo‐based fPINNs accurately solve fractional ODEs and PDEs while exposing an accuracy–cost trade‐off driven by the history‐dependent fractional derivative. Temporal collocation and shorter time windows are the most effective strategies for improving early‐time accuracy without unnecessary spatial refinement.
Donya Dabiri +4 more
wiley +1 more source
Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems Using Transfer Learning
Applied SciencesPhysics-Informed Neural Network (PINN) is a data-driven solver for partial and ordinary differential equations (ODEs/PDEs). It provides a unified framework to address both forward and inverse problems.
Abdul Hannan Mustajab +3 more
doaj +1 more source
Computing Skinning Weights via Convex Duality
Computer Graphics Forum, EarlyView.We present an alternate optimization method to compute bounded biharmonic skinning weights. Our method relies on a dual formulation, which can be optimized with a nonnegative linear least squares setup. Abstract We study the problem of optimising for skinning weights through the lens of convex duality.
J. Solomon, O. Stein
wiley +1 more source
The Hadamard-PINN for PDE inverse problems: Convergence with distant initial guesses
Examples and CounterexamplesThis paper presents the Hadamard-Physics-Informed Neural Network (H-PINN) for solving inverse problems in partial differential equations (PDEs), specifically the heat equation and the Korteweg–de Vries (KdV) equation.
Yohan Chandrasukmana +2 more
doaj +1 more source
Mesh Processing Non‐Meshes via Neural Displacement Fields
Computer Graphics Forum, EarlyView.Abstract Mesh processing pipelines are mature, but adapting them to newer non‐mesh surface representations—which enable fast rendering with compact file size—requires costly meshing or transmitting bulky meshes, negating their core benefits for streaming applications.
Yuta Noma +4 more
wiley +1 more source
Neumann problem on the semi-line for the Burgers equation [PDF]
, 2011 In this article, the Neumann problem on the semi-line for the Burgers equation is considered. The problem is reduced to a nonlinear integral equation in one independent variable, whose unique solution is proven to exist for small time.
Sommacal, Matteo, de Lillo, Silvana
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