Category: physics
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Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction
Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction Multiple hypothesis testing, P-values, and Monte Carlo The post Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction appeared first on Towards Data Science. Marco Hening Tallarico Go to original source
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Stochastic Differential Equations and Temperature — NASA Climate Data pt. 2
Stochastic Differential Equations and Temperature — NASA Climate Data pt. 2 The Ornstein-Uhlenbeck process in Python The post Stochastic Differential Equations and Temperature — NASA Climate Data pt. 2 appeared first on Towards Data Science. Marco Hening Tallarico Go to original source
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Physics-Informed Neural Networks for Inverse PDE Problems
Physics-Informed Neural Networks for Inverse PDE Problems Solving the Heat Equation using DeepXDE. The post Physics-Informed Neural Networks for Inverse PDE Problems appeared first on Towards Data Science. Marco Hening Tallarico Go to original source
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Exploratory Data Analysis: Gamma Spectroscopy in Python
Exploratory Data Analysis: Gamma Spectroscopy in Python Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source
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Essential Review Papers on Physics-Informed Neural Networks: A Curated Guide for Practitioners
Essential Review Papers on Physics-Informed Neural Networks: A Curated Guide for Practitioners Staying on top of a fast-growing research field is never easy. I face this challenge firsthand as a practitioner in Physics-Informed Neural Networks (PINNs). New papers, be they algorithmic advancements or cutting-edge applications, are published at an accelerating pace by both academia and…
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Reinforcement Learning with PDEs
Reinforcement Learning with PDEs Previously we discussed applying reinforcement learning to Ordinary Differential Equations (ODEs) by integrating ODEs within gymnasium. ODEs are a powerful tool that can describe a wide range of systems but are limited to a single variable. Partial Differential Equations (PDEs) are differential equations involving derivatives of multiple variables that can cover…
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Qubits Explained: Everything You Need to Know
Qubits Explained: Everything You Need to Know A deep dive into the building block of quantum computers. Continue reading on Towards Data Science » Sara A. Metwalli Go to original source
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Predicting a Ball Trajectory
Predicting a Ball Trajectory Polynomial Fit in Python with NumPy Continue reading on Towards Data Science » Florian Trautweiler Go to original source