63 citations · 129 across the 6 of their papers we have counts for
6 papers
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
Pongpisit Thanasutives, Ken-ichi Fukui
Data-driven discovery of partial differential equations (PDEs) has emerged as a promising approach for deriving governing physics when domain knowledge about observed data is limit…
On uncertainty-penalized Bayesian information criterion
Pongpisit Thanasutives, Ken-ichi Fukui
The uncertainty-penalized information criterion (UBIC) has been proposed as a new model-selection criterion for data-driven partial differential equation (PDE) discovery. In this p…
Adaptive Uncertainty-Guided Model Selection for Data-Driven PDE Discovery
Pongpisit Thanasutives, Takashi Morita, Masayuki Numao +1
We propose a new parameter-adaptive uncertainty-penalized Bayesian information criterion (UBIC) to prioritize the parsimonious partial differential equation (PDE) that sufficiently…
Noise-aware Physics-informed Machine Learning for Robust PDE Discovery
Pongpisit Thanasutives, Takashi Morita, Masayuki Numao +1
This work is concerned with discovering the governing partial differential equation (PDE) of a physical system. Existing methods have demonstrated the PDE identification from finit…
Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations
Pongpisit Thanasutives, Masayuki Numao, Ken-ichi Fukui
Recently, researchers have utilized neural networks to accurately solve partial differential equations (PDEs), enabling the mesh-free method for scientific computation. Unfortunate…
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
Pongpisit Thanasutives, Ken-ichi Fukui, Masayuki Numao +1
In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFA…