Sökning: "numerical differentiation"
Visar resultat 1 - 5 av 17 uppsatser innehållade orden numerical differentiation.
1. Simulations of a self-stabilizing fully submerged hydrofoil
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : Two models of a self-stabilizing hydrofoil system is developed where the effects from the struts and hydrofoil give torques for angular rotations. Lifting line theory for the hydrofoil which can twist is used. Nonlinear versions of the models are also developed and compared to find that the linear models use valid approximations. LÄS MER
2. Application of Physics-Informed Neural Networks for Galaxy Dynamics
Master-uppsats, Linnéuniversitetet/Institutionen för fysik och elektroteknik (IFE)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
3. Navigating the Sea of Sameness : Exploring Product Differentiation Strategies within the Swedish Nicotine Pouch Market
Master-uppsats, Uppsala universitet/Företagsekonomiska institutionenSammanfattning : In light of the exponential growth of the nicotine pouch industry, major tobacco-industry players have shown increasing interest in newer nicotine and tobacco products, despite the lack of discernible differences them. Therefore, this study aims to investigate the differentiation strategies of companies within the Swedish nicotine pouch market, and further, the long-term competitiveness of these strategies. LÄS MER
4. Optimizing First-Order Method Parameters via Differentiation of the Performance Estimation Problem
Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknikSammanfattning : This thesis treats the problem of finding optimal parameters for first-order optimization methods. In part, we use the Performance Estimation Problem (PEP), a framework for convergence analysis of first-order optimization methods. LÄS MER
5. Physics-informed Neural Networks for Biopharma Applications
Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för fysikSammanfattning : 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