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Found 7 papers, 2 papers with code
Neural Partial Differential Equations with Functional Convolution
We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs.
VortexNet: Learning Complex Dynamic Systems with Physics-Embedded Networks
Since the number of such vortices are much smaller than that of the Eulerian, grid discretization, this Lagrangian discretization in essence encodes the system dynamics on a compact physics-based latent space.
Nonseparable Symplectic Neural Networks
The enabling mechanics of our approach is an augmented symplectic time integrator to decouple the position and momentum energy terms and facilitate their evolution.
Sparse Symplectically Integrated Neural Networks
We introduce Sparse Symplectically Integrated Neural Networks (SSINNs), a novel model for learning Hamiltonian dynamical systems from data.
RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data
The ability of our model to predict long-term discontinuity from a short window of continuous training data is in general considered impossible using traditional machine learning approaches.
Neural Vortex Method: from Finite Lagrangian Particles to Infinite Dimensional Eulerian Dynamics
To tackle this challenge, we propose a novel learning-based framework, the Neural Vortex Method (NVM), which builds a neural-network description of the Lagrangian vortex structures and their interaction dynamics to reconstruct the high-resolution Eulerian flow field in a physically-precise manner.
Symplectic Neural Networks in Taylor Series Form for Hamiltonian Systems
We propose an effective and lightweight learning algorithm, Symplectic Taylor Neural Networks (Taylor-nets), to conduct continuous, long-term predictions of a complex Hamiltonian dynamic system based on sparse, short-term observations.
