- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
This volume gathers peer-reviewed papers from the workshop Scientific Machine Learning: Emerging Topics, held at SISSA in Trieste, Italy. The event gathered leading researchers in mathematics, algorithms, and machine learning. Its goal was to advance the synergy between data-driven models and scientific computing, promoting robust, interpretable, and scalable methods. The works reflect major trends in scientific machine learning (SciML), including optimization, physics-informed learning, neural graph/operators/ODE, transformers, and generative models. Contributions propose physics-based constrained neural networks, advancements in optimization and model reduction, and applications across power systems, chemical kinetics, and biomechanics. Topics span from hybrid models for image classification to generative compression and neural operators for high-dimensional systems. Blending theory and practice, the volume captures the diversity and innovation shaping modern SciML.
This volume is addressed to researchers and will provide readers with insight into the current state of the field, sparks new ideas, and encourages further research at the rich intersection of machine learning, mathematics, and scientific computing.
Contents
Chapter 1. Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks.- Chapter 2. Discovering Partially Known Ordinary Differential Equations: a Case Study on the Chemical Kinetics of Cellulose Degradation.- Chapter 3. Deep Unfolding for Scientific Computing on Embedded Systems.- Chapter 4. Non-Asymptotic Analysis of Projected Gradient Descent for Physics-Informed Neural Networks.- Chapter 5. MILP Initialization for Power Transformer Dynamic Thermal Modeling with PINNs.- Chapter 6. 3D point cloud generation for surface representation.- Chapter 7. Generative Models for Parameter Space Reduction applied to Reduced Order Modelling.- Chapter 8. High-Fidelity Description of Platelet Deformation Using a Neural Operator.- Chapter 9. Nonlinear reduction strategies for data compression: a comprehensive comparison from Diffusion to Advection problems.- Chapter 10. Model Reduction for Transport-Dominated Problems via Cross-Correlation Based Snapshot Registration.
