Derives non-asymptotic 2-norm and infinity-norm error bounds for deterministic and stochastic variants of OPTQ and Qronos PTQ algorithms.
Strang , The discrete cosine transform , SIAM Review, 41 (1999), pp
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Controlled benchmarks on Burgers, Darcy, Allen-Cahn and Navier-Stokes problems show grid unknowns favor discrete adjoint while neural representations favor PINNs, with PINNs cheaper for time-dependent cases and a hybrid strategy recovering adjoint accuracy at lower cost.
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Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
Derives non-asymptotic 2-norm and infinity-norm error bounds for deterministic and stochastic variants of OPTQ and Qronos PTQ algorithms.
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Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems
Controlled benchmarks on Burgers, Darcy, Allen-Cahn and Navier-Stokes problems show grid unknowns favor discrete adjoint while neural representations favor PINNs, with PINNs cheaper for time-dependent cases and a hybrid strategy recovering adjoint accuracy at lower cost.