A5087073352- Model Reduction and Neural Networks
- Nonlinear Partial Differential Equations
- Advanced Mathematical Modeling in Engineering
- Context-Aware Activity Recognition Systems
- Neural Networks and Applications
- Advanced Numerical Methods in Computational Mathematics
- Time Series Analysis and Forecasting
- Plant Water Relations and Carbon Dynamics
- Contact Mechanics and Variational Inequalities
- IoT and Edge/Fog Computing
- Neural Networks and Reservoir Computing
- Probabilistic and Robust Engineering Design
- Soil and Unsaturated Flow
- Nuclear Engineering Thermal-Hydraulics
University of Naples Federico II
2022-2024
Abstract Nowadays, in the Scientific Machine Learning (SML) research field, traditional machine learning (ML) tools and scientific computing approaches are fruitfully intersected for solving problems modelled by Partial Differential Equations (PDEs) science engineering applications. Challenging SML methodologies new computational paradigms named Physics-Informed Neural Networks (PINNs). PINN has revolutionized classical adoption of ML computing, representing a novel class promising...
Abstract We prove the local boundedness for solutions to a class of obstacle problems with non-standard growth conditions. The novelty here is that we are able establish under sharp bound on gap between exponents.
We prove the local boundedness for solutions to a class of obstacle problems with non-standard growth conditions. The novelty here is that we are able establish under sharp bound on gap between exponents.