Essays about: "physics-informed neural networks"
Showing result 1 - 5 of 8 essays containing the words physics-informed neural networks.
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1. Evaluation of Physics Informed Neural Networks in engineering simple structural analysis problems
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Neural Networks have found many applications for a long time in Machine Learning in different disciplines, and have especially flourished in the last decade because of the ever-increasing processing power especially from GPUs. Because of their ability to operate as universal function approximators and model nonlinear processes, attempts have been made in recent years to be also used for modeling partial differential equation (PDE) solutions. READ MORE
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2. Application of Physics-Informed Neural Networks for Galaxy Dynamics
University essay from Linnéuniversitetet/Institutionen för fysik och elektroteknik (IFE)Abstract : Developing efficient and accurate numerical methods to simulate dynamics of physical systems has been an everlasting challenge in computational physics. Physics-Informed Neural Networks (PINNs) are neural networks that encode laws of physics into their structure. READ MORE
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3. Solving Partial Differential Equations With Neural Networks
University essay from Uppsala universitet/Matematiska institutionenAbstract : In this thesis three different approaches for solving partial differential equa-tions with neural networks will be explored; namely Physics-Informed NeuralNetworks, Fourier Neural Operators and the Deep Ritz method. Physics-Informed Neural Networks and the Deep Ritz Method are unsupervised machine learning methods, while the Fourier Neural Operator is a supervised method. READ MORE
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4. Physics-Informed Neural Networks for Liquid Chromatography
University essay from Umeå universitet/Institutionen för fysikAbstract : Liquid chromatography is a technique used to separate and purify components of a mixture. The method is frequently used in the biomedicine industry and life science to discover and develop new drugs. Here liquid chromatography can separate the drug candidate from its byproducts. READ MORE
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5. A Physics-Informed Deep Learning Framework for Solving Inverse Problems in Epidemiology
University essay from KTH/Optimeringslära och systemteoriAbstract : This thesis develops and evaluates a physics-informed neural network (PINN) modelling framework for solving inverse problems in epidemiology. The PINN works by modifying the standard mean squared error loss function of the neural network, by adding a term penalizing deviations from a given compartmental model's system of ordinary differential equations. READ MORE
