Sökning: "PINNs"

Visar resultat 1 - 5 av 6 uppsatser innehållade ordet PINNs.

1. 1. Evaluation of Physics Informed Neural Networks in engineering simple structural analysis problems

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Georgios Nentidis; [2023]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER

3. 2. Application of Physics-Informed Neural Networks for Galaxy Dynamics

   Master-uppsats, Linnéuniversitetet/Institutionen för fysik och elektroteknik (IFE)

   Författare :Lucas Barbier; [2023]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER

4. 3. Physics-Informed Neural Networks for Liquid Chromatography

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för fysik

   Författare :Pontus Söderström; [2022]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER

5. 4. A Physics-Informed Deep Learning Framework for Solving Inverse Problems in Epidemiology

   Master-uppsats, KTH/Optimeringslära och systemteori

   Författare :Magnus Tronstad; [2022]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER

7. 5. Physics-informed Neural Networks for Biopharma Applications

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för fysik

   Författare :Linnéa Cedergren; [2021]
   Nyckelord :PINN; PINNs; physics-informed neural networks; neural networks; machine learning; sartorius; CSTR;

   Sammanfattning : Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations into the training of neural networks, with the aim of bringing the best of both worlds. This project used a mathematical model describing a Continuous Stirred-Tank Reactor (CSTR), to test two possible applications of PINNs. LÄS MER