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Found 12 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.
Simultaneous Optimal System and Controller Design for Multibody Systems with Joint Friction using Direct Sensitivities
This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach for gradient computation.
Adversarial Training Using Feedback Loops
A neural network architecture that incorporates feedback control, named Feedback Neural Networks, is proposed.
Neural Network Reduction with Guided Regularizers
Regularization techniques such as $\mathcal{L}_1$ and $\mathcal{L}_2$ regularizers are effective in sparsifying neural networks (NNs).
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.
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders
The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation.
A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering
The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension.
Methodology Numerical Analysis Numerical Analysis
A Machine Learning Approach to Adaptive Covariance Localization
In typical weather forecasting applications, the model state space has dimension $10^{9}-10^{12}$, while the ensemble size typically ranges between $30-100$ members.
Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques
Reduced order models are computationally inexpensive approximations that capture the important dynamical characteristics of large, high-fidelity computer models of physical systems.
