AMIGO is an end-to-end differentiable forward model of JWST AMI that corrects detector systematics to recover high-precision astrometry and detect close high-contrast companions.
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Relaxation techniques applied after each time step to IMEX Runge-Kutta schemes ensure that fully discrete Schrödinger-Poisson systems conserve mass and satisfy energy balance equations up to rounding errors.
Curvature-aware optimizers such as natural gradient and self-scaling BFGS/Broyden accelerate PINN convergence and accuracy on PDEs including Helmholtz, Stokes, Burgers, and Euler equations plus stiff ODEs, with new model formulations and batched scaling.
citing papers explorer
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AMIGO: a Data-Driven Calibration of the JWST Interferometer
AMIGO is an end-to-end differentiable forward model of JWST AMI that corrects detector systematics to recover high-precision astrometry and detect close high-contrast companions.
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Efficient High-order Mass-conserving and Energy-balancing Schemes for Schr\"odinger-Poisson Equations
Relaxation techniques applied after each time step to IMEX Runge-Kutta schemes ensure that fully discrete Schrödinger-Poisson systems conserve mass and satisfy energy balance equations up to rounding errors.
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Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
Curvature-aware optimizers such as natural gradient and self-scaling BFGS/Broyden accelerate PINN convergence and accuracy on PDEs including Helmholtz, Stokes, Burgers, and Euler equations plus stiff ODEs, with new model formulations and batched scaling.
- Complex surface patterning in homo- and heteroepitaxial contexts: (simultaneous) step bunching and step meandering