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Physics Informed Machine Learning for Soft Matter

Publicly Offered Research

Project AreaFoundation of "Machine Learning Physics" --- Revolutionary Transformation of Fundamental Physics by A New Field Integrating Machine Learning and Physics
Project/Area Number 25H01536
Research Category

Grant-in-Aid for Transformative Research Areas (A)

Allocation TypeSingle-year Grants
Review Section Transformative Research Areas, Section (II)
Research InstitutionKyoto University

Principal Investigator

MOLINA JOHN  京都大学, 工学研究科, 助教 (20727581)

Project Period (FY) 2025-04-01 – 2027-03-31
Project Status Granted (Fiscal Year 2025)
Budget Amount *help
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2025: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
KeywordsMachine Learning / Soft Matter / Inverse Problem / Polymer Melts / Stokes Flow
Outline of Research at the Start

We will develop Machine-Learning assisted simulation methods for (A) entangled polymer melt flows and (B) Stokes flows (i.e., flows at small scales). For (A), we will infer the relationship between microscopic and macroscopic degrees of freedom, as well as optimize the flow. For (B) we will develop a probabilistic framework to infer the flow solution in complex environments, with moving boundaries, from partial/noisy data. These methods will be used to analyze (A) industrial polymer processing flows, as well as (B) Stokes flows typical of biological or colloidal systems.

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Published: 2025-04-17   Modified: 2025-06-20  

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