Hugo Carrillo

Hugo Carrillo, Carlos Parés

A new family of high order methods for systems of conservation laws are introduced: the Compact Approximate Taylor (CAT) methods. These methods are based on centered (2p + 1)-point stencils where p is an arbitrary integer. We prove that the order of accuracy is 2p and that CAT methods are an extension of high-order Lax-Wendroff methods for linear problems. Due to this, they are linearly L2-stable under a CFL<1 condition. In order to prevent the spurious oscillations that appear close to discontinuities two shock-capturing techniques have been considered: a fux-limiter technique (FL-CAT methods) and WENO reconstruction for the frst time derivative (WENO-CAT methods). We follow the WENO-Lax Wendroff Approximate Taylor method of Zorio, Baeza and Mullet (2017) in the second approach. A number of test cases are considered to compare these methods with other WENO-based schemes: the linear transport equation, Burgers equation, and the 1D compressible Euler system are considered. Although CAT methods present an extra computational cost due to the local character, this extra cost is compensated by the fact that they still give good solutions with CFL values close to 1.

Taco de Wolff, Hugo Carrillo, Luis Martí et al.

The carbon pump of the world's ocean plays a vital role in the biosphere and climate of the earth, urging improved understanding of the functions and influences of the ocean for climate change analyses. State-of-the-art techniques are required to develop models that can capture the complexity of ocean currents and temperature flows. This work explores the benefits of using physics-informed neural networks (PINNs) for solving partial differential equations related to ocean modeling; such as the Burgers, wave, and advection-diffusion equations. We explore the trade-offs of using data vs. physical models in PINNs for solving partial differential equations. PINNs account for the deviation from physical laws in order to improve learning and generalization. We observed how the relative weight between the data and physical model in the loss function influence training results, where small data sets benefit more from the added physics information.