Casey Murphy has fanned his passion for finance through years of writing about active trading, technical analysis, market commentary, exchange-traded funds (ETFs), commodities, futures, options, and ...
Abstract: In this study, physics-informed graph residual learning (PhiGRL) is proposed as an effective and robust deep learning (DL)-based approach for 3-D electromagnetic (EM) modeling. Extended from ...
Abstract: This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. For multi-robots to efficiently perform ...
This library is a generalization of SINDy, to be used for the reconstruction of dynamical systems with strong nonlinearities, which require the introduction of a combinatorial search in the elementary ...
This repository is an implementation of the paper entitled "On Representing Mixed-Integer Linear Programs by Graph Neural Networks." (ICLR 2023) The paper can be found here. Our codes are modified ...
What began with a focus on weather forecasting has evolved toward addressing errors in scientific modeling. In the collaborative environment of the Penn State Institute for Computational and Data ...
I will never forget this one day when Kevin was a preschooler. We had an IEP meeting, and one of his proposed math IEP goals was to be able to visualize and identify what 2 of something looks like or ...
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