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Found 8 papers, 1 papers with code
Improving the Adaptive Moment Estimation (ADAM) stochastic optimizer through an Implicit-Explicit (IMEX) time-stepping approach
The Adam optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates.
Precision Mars Entry Navigation with Atmospheric Density Adaptation via Neural Networks
Discrepancies between the true Martian atmospheric density and the onboard density model can significantly impair the performance of spacecraft entry navigation filters.
Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders
Data-driven reduced order modeling of chaotic dynamics can result in systems that either dissipate or diverge catastrophically.
A Sensor-Based Simulation Method for Spatiotemporal Event Detection
Human movements in urban areas are essential to understand human-environment interactions.
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction
A rapidly growing area of research is the use of machine learning approaches such as autoencoders for dimensionality reduction of data and models in scientific applications.
Physics-informed neural networks for PDE-constrained optimization and control
A fundamental problem in science and engineering is designing optimal control policies that steer a given system towards a desired outcome.
Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation
Surrogates constructed using adjoint information demonstrate superior performance on the 4D-Var data assimilation problem compared to a standard neural network surrogate that uses only forward dynamics information.
Investigation of Nonlinear Model Order Reduction of the Quasigeostrophic Equations through a Physics-Informed Convolutional Autoencoder
Reduced order modeling (ROM) is a field of techniques that approximates complex physics-based models of real-world processes by inexpensive surrogates that capture important dynamical characteristics with a smaller number of degrees of freedom.
