Sökning: "physically-based"
Visar resultat 1 - 5 av 50 uppsatser innehållade ordet physically-based.
1. Real-time Soft Body Simulation using Extended Position-Based Dynamics and Tetrahedral Deformation
Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : Background. Several methods have been used to simulate soft body deformation, such as mass-spring systems and position-based dynamics. This has been done using tetrahedral mesh models for preservation of shape and volume. LÄS MER
2. Anticipating Glacier Lake Outburst Floods (GLOFs): an impact-based forecasting framework for managing GLOF risks in Nepal.
Master-uppsats, Lunds universitet/Avdelningen för Riskhantering och SamhällssäkerhetSammanfattning : Glacier lake outburst floods (GLOFs) are an increasingly documented threat across the Himalayan region, wherein Nepal is situated. GLOFs involve a rapid discharge of water from a lake situated at the side, front, within, beneath, or on the surface of a glacier. LÄS MER
3. Applying spatially and temporally adaptive techniques for faster DEM-based snow simulation
Uppsats för yrkesexamina på avancerad nivå, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : Background. Physically-based snow simulation is computationally expensive and not yet applicable to real-time applications. Some of the prime factors for this cost are the complex physics, the large number of particles, and the small time step required for a high-quality and stable simulation. LÄS MER
4. Evaluation of Performance and Image Quality for Voxel Cone Tracing
Kandidat-uppsats, Mälardalens universitet/Akademin för innovation, design och teknikSammanfattning : Voxel cone tracing (VCT) is a rendering method designed to approximate global illumination in a fast and efficient way. Global illumination means to render not only the direct lighting of a scene but also light from indirect sources, simulating how light in the real-world tend to bounce around and illuminate even the areas that are occluded from a direct light source. LÄS MER
5. An evaluation of deep learning models for urban floods forecasting
Master-uppsats, KTH/GeoinformatikSammanfattning : Flood forecasting maps are essential for rapid disaster response and risk management, yet the computational complexity of physically-based simulations hinders their application for efficient high-resolution spatial flood forecasting. To address the problems of high computational cost and long prediction time, this thesis proposes to develop deep learning neural networks based on a flood simulation dataset, and explore their potential use for flood prediction without learning hydrological modelling knowledge from scratch. LÄS MER