Innovative Research Award

Yih-Chin Tai

National Cheng Kung University, Taiwan

Yih-Chin Tai
Affiliation	National Cheng Kung University
Country	Taiwan
Scopus ID	57211771208
Documents	78
Citations	1,491
h-index	19
Subject area	Mathematical Modeling
Event	Math Scientist Awards
ORCID	0000-0001-5925-8207

The Innovative Research Award recognizes research contributions that demonstrate methodological development, scientific relevance, and potential value to their respective disciplines. Yih-Chin Tai of National Cheng Kung University, Taiwan, is presented in this academic recognition profile in connection with research involving computational methods, fluid flows, and mathematical modeling. The publications listed below provide examples of work addressing numerical simulation, multiphase transport, and geotechnical processes. Bibliographic records and researcher metrics offer complementary perspectives on scholarly activity and research visibility. [1]

Abstract

This article presents an academic recognition profile for Yih-Chin Tai, whose supplied research record includes publications on free-surface flow simulation, grain–fluid interactions, and slope stability. These topics involve mathematical representations of complex physical systems and computational techniques for investigating their behavior. A 2024 article in Communications in Computational Physics addresses a neural particle method with interface tracking and adaptive refinement, while another publication in Advances in Water Resources examines grain–fluid flows over rugged terrain. A 2023 study in Water considers seepage effects and layered slope stability. [2] [3] [4]

Keywords

Mathematical modeling; computational fluid dynamics; particle methods; free-surface flows; multiphase flow; adaptive refinement; sediment transport; slope stability; numerical simulation; scientific computing.

Introduction

Mathematical modeling connects physical principles with analytical and computational tools to investigate systems that may be difficult to observe or reproduce experimentally. In fluid mechanics and geotechnical engineering, numerical models can help explore moving interfaces, interactions between particles and fluids, and changes in material stability. Reliable modeling requires appropriate mathematical formulations, numerical methods, and careful interpretation of results. The publications identified in this profile address different aspects of these challenges and provide a basis for describing their shared computational and applied-science context. [2] [3]

Research Profile

The supplied bibliographic records indicate research interests spanning computational fluid mechanics and geophysical flow processes. Particle-based simulation is relevant to problems involving deforming free surfaces, while grain–fluid models address the movement and interaction of solid and liquid phases. Slope-stability research considers how subsurface water processes may influence the behavior of layered terrain. These areas share a need to represent complex physical interactions through mathematical descriptions and numerical computation. The listed affiliation and researcher identifiers provide routes for checking the profile and its associated publication records. [1]

Research Contributions

The three supplied publications represent complementary methodological and applied directions:

• Adaptive particle simulation: The 2024 study presents a neural particle method incorporating interface tracking and adaptive particle refinement for free-surface flows. These features address the computational representation of evolving fluid boundaries. [2]
• Grain–fluid interaction: The 2024 article develops a two-phase approach to modeling grain–fluid flows with deposition and entrainment over rugged topography, a problem relevant to complex terrain and sediment movement. [3]
• Geotechnical stability: The 2023 study investigates the seepage hammer effect and its implications for the stability of layered infinite slopes, connecting water movement with a practical engineering concern. [4]

Publications

1. “A Neural Particle Method with Interface Tracking and Adaptive Particle Refinement for Free Surface Flows.” Communications in Computational Physics, June 2024. [2]
2. “Two-phase approach to modeling the grain-fluid flows with deposition and entrainment over rugged topography.” Advances in Water Resources, June 2024. [3]
3. “A Preliminary Study of the Seepage Hammer Effect and Its Impacts on the Stability of Layered Infinite Slope.” Water, May 2023. [4]

Research Impact

The supplied profile reports 78 documents, 1,491 citations, and an h-index of 19. These indicators offer a quantitative snapshot of publication output and citation activity, but their interpretation depends on the database, indexing coverage, and date of retrieval. They do not independently establish the quality or practical impact of individual studies. The publication topics nevertheless illustrate potential relevance to numerical methods, fluid mechanics, sediment transport, and geotechnical engineering. Readers should consult the current Scopus record and original articles to verify updated metrics and assess individual findings. [1]

Award Suitability

The listed research themes provide a basis for considering Yih-Chin Tai for recognition under an Innovative Research Award category associated with mathematical modeling and computational science. The combination of numerical-method development and applications to physical systems reflects the interdisciplinary nature of contemporary modeling research. Final award decisions should rely on the organizer’s evaluation criteria, verified bibliographic information, and an appropriate assessment of originality, methodological rigor, and contribution to the field. This profile describes potential alignment rather than independently certifying an award outcome.

Conclusion

Yih-Chin Tai’s supplied publication record covers computational particle methods, grain–fluid modeling, and slope-stability analysis. Together, these topics demonstrate the breadth of mathematical and numerical approaches used to study complex physical processes. The Innovative Research Award profile provides an overview of the identified research themes and bibliographic indicators while directing readers to the original publications and researcher records for further verification.

External Links

• ORCID Profile
• Scopus Author Profile
• Math Scientist Awards

References

1. Elsevier. (n.d.). Scopus author details: Yih-Chin Tai, Author ID 57211771208. Scopus. Profile metrics are reproduced from the supplied information and may change.
   https://www.scopus.com/authid/detail.uri?authorId=57211771208
2. “A Neural Particle Method with Interface Tracking and Adaptive Particle Refinement for Free Surface Flows.” (2024). Communications in Computational Physics. DOI: 10.4208/cicp.oa-2023-0235.
   https://doi.org/10.4208/cicp.oa-2023-0235
3. “Two-phase approach to modeling the grain-fluid flows with deposition and entrainment over rugged topography.” (2024). Advances in Water Resources. DOI: 10.1016/j.advwatres.2024.104691.
   https://doi.org/10.1016/j.advwatres.2024.104691
4. “A Preliminary Study of the Seepage Hammer Effect and Its Impacts on the Stability of Layered Infinite Slope.” (2023). Water, 15(10), article 1832. DOI: 10.3390/w15101832.
   https://doi.org/10.3390/w15101832
5. ORCID. (n.d.). Yih-Chin Tai: ORCID researcher identifier. ORCID.
   https://orcid.org/0000-0001-5925-8207
6. Math Scientist Awards. (n.d.). Official awards website.
   mathscientists.com