3 papers
Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics
Fabio Pereira dos Santos, Renato Portugal, Júlio de Castro Vargas Fernandes +1
Physics-informed neural networks (PINNs) have emerged as a versatile approach for solving nonlinear partial differential equations (PDEs), yet achieving high accuracy efficiently u…
Shearlet Neural Operators for Anisotropic-Shock-Dominated and Multi-scale parametric partial differential equations
Fabio Pereira dos Santos, Julio de Castro Vargas Fernandes, Adriano Mauricio de Almeida Cortes
Neural operators have emerged as powerful data-driven surrogates for learning solution operators of parametric partial differential equations (PDEs). However, widely used Fourier N…
Statistical machine learning tools for probabilistic closures of turbulence models
Julia Domingues Lemos, Fabio Pereira dos Santos
Turbulent flow remains a challenging subject, despite extensive efforts to find analytical descriptions. Modeling small scales of motion is crucial for saving time and resources in…