Windowing and buffer hard-constrained PINNs enforce interface physics by design, yielding higher interface fidelity than soft-constrained baselines on elliptic benchmarks.
Lee, Least-squares enhanced physics-informed learning for singular and ill-posed partial differential equations, Computers & Mathematics with Applications 206 (2026) 301–315
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Hard-constrained Physics-informed Neural Networks for Interface Problems
Windowing and buffer hard-constrained PINNs enforce interface physics by design, yielding higher interface fidelity than soft-constrained baselines on elliptic benchmarks.