Best Researcher Award
Der Liang Young
National Taiwan University, Taiwan
Der Liang Young is a researcher affiliated with National Taiwan University whose documented scholarly work connects computational methods, numerical analysis, meshless techniques, and machine-learning approaches for mathematical and engineering problems. Bibliographic records identify research involving radial basis functions, partial differential equations, physics-informed learning, and residual neural-network architectures. [1]
Abstract
Der Liang Young’s academic profile reflects sustained research activity at the intersection of numerical computation, engineering analysis, and machine learning. His documented publications include work on radial basis function methods, partial differential equations, meshless computation, and neural-network approaches for interpolation and inverse problems. Bibliographic sources associate him with National Taiwan University and ORCID identifier 0000-0002-3611-2982. [2]
Keywords
- Machine Learning
- Numerical Analysis
- Radial Basis Functions
- Physics-Informed Neural Networks
- Partial Differential Equations
- Meshless Methods
Introduction
Der Liang Young’s research profile is situated within computational engineering and mathematical modeling, with published work addressing numerical methods and machine-learning techniques for complex problems. His research includes collaborations involving National Taiwan University and applications of advanced computational approaches to interpolation, inverse problems, and partial differential equations.[3]
Research Profile
Young’s documented research spans numerical computation, computational mechanics, meshless methods, radial basis functions, and machine learning. Bibliographic records identify publications addressing partial differential equations, piezoelectric problems, quasicrystal plates, and neural-network methods. His ORCID record provides a persistent identifier supporting the organization and attribution of his scholarly research outputs. [1]
Research Contributions
His published contributions include numerical formulations for differential equations, local radial basis function collocation, and machine-learning architectures for interpolation and inverse problems. A 2024 study with collaborators examined a power-enhanced residual network for function approximation and physics-informed inverse problems, illustrating the connection between neural-network design and computational mathematics. [2]
Publications
Available bibliographic records document publications by Young and collaborators in journals and conference proceedings covering computational mathematics and engineering. Examples include work on two-step MPS-MFS ghost point methods, local radial basis function collocation for piezoelectric problems, and power-enhanced residual networks. These publications demonstrate continuity between numerical methods and contemporary computational learning approaches. [3]
Research Impact
The supplied Scopus metrics record 200 documents, 4,538 citations, and an h-index of 38 for the identified author profile. These bibliometric indicators provide quantitative measures of publication activity and citation visibility, while individual publications demonstrate application of the research across numerical analysis, engineering computation, and machine-learning methodology. [1]
Award Suitability
The documented publication record, research themes, institutional affiliation, and supplied bibliometric indicators provide relevant evidence for consideration under a Best Researcher Award framework. The profile combines established computational research with machine-learning applications, while indexed scholarly outputs and citation measures offer quantitative information that can support an independent award-review process.[3]
Conclusion
Der Liang Young’s documented scholarly profile combines numerical analysis, computational engineering, meshless methods, and machine-learning research. His publication record includes studies of advanced numerical algorithms and neural-network methodologies, while the supplied bibliometric data indicate substantial indexed research activity and citation visibility. [2]
External Links
References
- Biochemical and anatomical characterization of forepaw adjusting steps in rat models of Parkinson’s disease: studies on medial forebrain bundle and striatal lesions.
https://pubmed.ncbi.nlm.nih.gov/10197780/
- Implicit Branch Selection in Physics-Informed Neural Networks for an Underdetermined Exterior Laplace Problem: Potential Flow Around a Circular Cylinder with Weak Far-Field Regularization.
https://www.researchgate.net/publication/408048483_
- A BC–GE-embedded strong-form meshless method for three-dimensional incompressible Navier–Stokes flows
https://link.springer.com/article/10.1007/s00707-026-04874-4