4 papers
Intrinsic-Metric Physics-Informed Neural Networks (IM-PINN) for Reaction-Diffusion Dynamics on Complex Riemannian Manifolds
Julian Evan Chrisnanto, Salsabila Rahma Alia, Nurfauzi Fadillah +1
Simulating nonlinear reaction-diffusion dynamics on complex, non-Euclidean manifolds remains a fundamental challenge in computational morphogenesis, constrained by high-fidelity me…
ASPEN: An Adaptive Spectral Physics-Enabled Network for Ginzburg-Landau Dynamics
Julian Evan Chrisnanto, Nurfauzi Fadillah, Yulison Herry Chrisnanto
Physics-Informed Neural Networks (PINNs) have emerged as a powerful, mesh-free paradigm for solving partial differential equations (PDEs). However, they notoriously struggle with s…
Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics
Julian Evan Chrisnanto, Salsabila Rahma Alia, Yulison Herry Chrisnanto +1
Ecological systems exhibit complex multi-scale dynamics that challenge traditional modeling. New methods must capture temporal oscillations and emergent spatiotemporal patterns whi…
Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization
Yulison Herry Chrisnanto, Julian Evan Chrisnanto
Cut order planning (COP) is a critical challenge in the textile industry, directly impacting fabric utilization and production costs. Conventional methods based on static heuristic…