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Found 8 papers, 1 papers with code
Stochastic force inference via density estimation
Inferring dynamical models from low-resolution temporal data continues to be a significant challenge in biophysics, especially within transcriptomics, where separating molecular programs from noise remains an important open problem.
Learning locally dominant force balances in active particle systems
We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems.
Learning deterministic hydrodynamic equations from stochastic active particle dynamics
We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories.
Parallel Discrete Convolutions on Adaptive Particle Representations of Images
Here, we provide the algorithmic building blocks required to efficiently and natively process APR images using a wide range of algorithms that can be formulated in terms of discrete convolutions.
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks
We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Networks (PINNs).
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations
Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e. g., in applications like turbulence, combustion, and shock propagation.
Learning physically consistent mathematical models from data using group sparsity
We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetries when learning or inferring differential-equation models from measurement data.
Stability selection enables robust learning of partial differential equations from limited noisy data
We show that in particular the combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast, parameter-free, and robust computational framework for PDE inference that outperforms previous algorithmic approaches with respect to recovery accuracy, amount of data required, and robustness to noise.
