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This paper proposes NeTMY, an amortization-free coordinate neural field for inverse problems in NV-center quantum sensing, using a corrected forward model and sparse reconstruction losses to overcome center-collapse pathologies.
This paper introduces MuFiNNs, a hierarchical multi-fidelity neural network framework for predicting 3D flame wrinkling and turbulent burning velocity using sparse experimental data. The approach integrates low-fidelity physical trends with high-fidelity corrections to enable robust prediction and extrapolation in data-limited combustion regimes.
This paper establishes quantitative Sobolev approximation bounds for neural operators, proving that operators can be uniformly approximated with explicit complexity-error relations. It validates these theoretical bounds using Fourier Neural Operators on the Burgers' equation, demonstrating that Sobolev-space approximation theory accurately predicts scaling behavior.
This paper introduces MeLISA, a latent-free autoregressive generative surrogate for forecasting high-dimensional physical dynamics that uses pixel-space MeanFlow to achieve efficient one-step generation. It demonstrates superior long-horizon statistical accuracy and inference speed compared to neural operators on turbulent flow benchmarks.