Author

C. Tsgoev

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Open access Oct 2026

MultiPINN: A Modular Framework for Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) approximate solutions of differential equations by embedding governing equations and boundary conditions into the training objective. Controlled PINN comparisons are difficult because geometry representation, collocation sampling, loss construction, neural architecture, and opti...

C. Tsgoev, V. Kalinin, M. Bratenkov et al. · Machine Learning and Knowled... · 0 citations

Oct 2026

GINTO: Geometry-informed transformer neural operator for fluid flows on arbitrary domains

Generalizing neural operators across varying geometries remains a central challenge in scientific machine learning. While most operator-learning frameworks assume fixed geometries and parametric variability in boundary conditions or coefficients, many fluid-dynamics problems require prediction across distinct domains....

C. Tsgoev, M. Bratenkov, Victoria Svyatkina et al. · The Physics of Fluids · 0 citations