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INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations Open
For numerical design, the development of efficient and accurate surrogate models is paramount. They allow us to approximate complex physical phenomena, thereby reducing the computational burden of direct numerical simulations. We propose I…
View article: Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations Open
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple se…
Stability of implicit neural networks for long-term forecasting in dynamical systems Open
Forecasting physical signals in long time range is among the most challenging tasks in Partial Differential Equations (PDEs) research. To circumvent limitations of traditional solvers, many different Deep Learning methods have been propose…
Multi-scale Physical Representations for Approximating PDE Solutions with Graph Neural Operators Open
Representing physical signals at different scales is among the most challenging problems in engineering. Several multi-scale modeling tools have been developed to describe physical systems governed by \emph{Partial Differential Equations} …