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Research questionHow reliably can sparse PDE discovery recover phase-ordering dynamics from limited, noisy spatiotemporal data?Sparse PDE discovery infers coarse-grained evolution equations from spatiotemporal observations. Limited or noisy data and the choice of candidate terms can affect both which dynamics are identified and how accurately their coefficients are recovered.
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Data Driven Equation Discovery for Phase-Ordering Dynamics : From Allen Cahn to the Ising ModelThe study examines PDE-SINDy using the Allen–Cahn equation as a benchmark and the Glauber spin-flip Ising model as a microscopic system. It varies data availability, candidate-library size, and noise, and assesses term identification, coefficient recovery, and reproduction of phase separation and coarsening dynamics.research paper · Sep 3, 2026
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