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3 days ago · Abstract. This work addresses the synthesis of optimal feedback control laws via machine learning. In particular, the Averaged Feedback Learning Scheme ...
7 days ago · This work studies Stochastic Optimal Control (SOC) problems where the state evolves in continuous time but observations are available only in discrete time. The.
6 days ago · Neural-network based control methods trained using deep reinforcement learning (RL) have achieved state-of-the-art performance on challenging non-linear ...
6 days ago · Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that can ...
Missing: Formulation | Show results with:Formulation
5 days ago · Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight ...
7 days ago · Successful application of a depth estimation model for quality control ... In plot C the average cross-sectional area of the training data as well as the average ...
17 hours ago · We gathered and examined solubility data for more than 84,000 molecules, with differing levels of estimated average reproducibility across laboratories.
3 days ago · We develop a Mean-Field (MF) view of the learning dynamics of overparametrized Artificial Neural Networks (NN) under data symmetric in law wrt the action of a ...
Missing: Formulation Deep
7 days ago · We propose conditioning field initialization for neural network based topology optimization. In this work, we focus on (1) improving upon existing neural ...
3 days ago · The fundamentals of fault diagnosis include defining its significance and exploring traditional methods in fault detection and diagnosis. This review paper has ...