The Hadamard-PINN for PDE inverse problems: Convergence with distant initial guesses
This 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. H-PINN addresses challenges in convergence and accuracy when initial parameter gue...
Saved in:
Main Authors: | Yohan Chandrasukmana, Helena Margaretha, Kie Van Ivanky Saputra |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-06-01
|
Series: | Examples and Counterexamples |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666657X25000023 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evolution of CFD as an engineering science. A personal perspective with emphasis on the finite volume method
by: Runchal, Akshai Kumar
Published: (2024-06-01) -
Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics
by: Stefan Schoder
Published: (2025-01-01) -
Magnetic Resonance Spectroscopy (MRS) transforming multiple sclerosis (MS) diagnosis
by: Landoline Bonnin, et al.
Published: (2025-03-01) -
Modulation of Second Messenger Signaling in the Brain Through PDE4 and PDE5 Inhibition: Therapeutic Implications for Neurological Disorders
by: Min Kyu Park, et al.
Published: (2025-01-01) -
Physics-Informed Neural Network-Based Input Shaping for Vibration Suppression of Flexible Single-Link Robots
by: Tingfeng Li, et al.
Published: (2025-01-01)