Latent assimilation with implicit neural representations for unknown dynamics
arXiv:2309.09574 · doi:10.1016/j.jcp.2024.112953
Abstract
Data assimilation is crucial in a wide range of applications, but it often faces challenges such as high computational costs due to data dimensionality and incomplete understanding of underlying mechanisms. To address these challenges, this study presents a novel assimilation framework, termed Latent Assimilation with Implicit Neural Representations (LAINR). By introducing Spherical Implicit Neural Representations (SINR) along with a data-driven uncertainty estimator of the trained neural networks, LAINR enhances efficiency in assimilation process. Experimental results indicate that LAINR holds certain advantage over existing methods based on AutoEncoders, both in terms of accuracy and efficiency.
arXiv comment40 pages
References in corpus (16)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
- DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
- DGM: A deep learning algorithm for solving partial differential equations
- Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
- PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
- WeatherBench: A benchmark dataset for data-driven weather forecasting
- Dedalus: A Flexible Framework for Numerical Simulations with Spectral Methods
- Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere
- Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench
- Using machine learning to correct model error in data assimilation and forecast applications
- A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling
- Combining distribution-based neural networks to predict weather forecast probabilities
- Integrating Recurrent Neural Networks with Data Assimilation for Scalable Data-Driven State Estimation
- COIN++: Neural Compression Across Modalities
- Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation