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: The prediction of multivariate time series (MTS) has increasingly captured substantial interest in practical domains such as chemical process flow and data analysis, which representing a ...
HHsim is a graphical simulation of a section of excitable neuronal membrane using the Hodgkin-Huxley equations. It provides full access to the Hodgkin-Huxley parameters, membrane parameters, stimulus ...
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“I was curious to establish a baseline for when LLMs are effectively able to solve open math problems compared to where they ...
Learn to create a yield curve in Excel and understand its implications for interest rate forecasting. Follow our simple guide ...
The information and materials presented here are intended to provide a description of the course goals for current and prospective students as well as others who are interested in our courses. It is ...
The information presented here is intended to describe the course goals for current and prospective students as well as others who are interested in our courses. It is not intended to replace the ...
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 ...
A mathematical model to predict microbial growth in milk was developed and analyzed. The model consists of a system of two differential equations of first order. The equations are based on physical ...
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