Understanding the outbreak dynamics of COVID-19 through the lens of mathematical models is an elusive but significant goal. Within only half a year, the COVID-19 pandemic has resulted in more than 19 million reported ...

We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy function and the dissipation potential with ...

Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation. However, the remarkable flexibility offered by data-driven architectures would suggest to extend this ...

Understanding patterns and energy-related data in photovoltaic systems is one of the key tasks in energy generation and distribution. In fact, the use of data-driven tools and predictive learning models can support the ...

Eight different models to represent the effect of friction in control valves are presented: four models based on physical principles and four empirical ones. The physical models, both static and dynamic, have the same ...

In this talk we present the Fluid Concepts cognitive model, developed by Douglas Hofstadter and his students over the last 30 years. Models in the Fluid Concepts tradition aim to provide a psychologically plausible description ...

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands ...