Machine Learning for Inverse Problems and Data Assimilation
Not on the map. Why?
Abstract (arXiv)
The aim of this book is to demonstrate the potential for ideas in machine learning to impact on the fields of inverse problems and data assimilation. The perspective is one that is primarily aimed at researchers from inverse problems and/or data assimilation who wish to see a mathematical presentation of machine learning as it pertains to their fields. As a by-product, we include a succinct mathematical treatment of various fundamental underpinning topics in machine learning, and adjacent areas of (computational) mathematics.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Oct 2024.
Builds on
0 of 0 references.
No references here yet.
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 6.
7 papers.
- 2026Learning Probabilistic Filters with Strictly Proper Scoring Rulescited by 6
- 2026Amortized Filtering and Smoothing with Conditional Normalizing Flowscited by 2
- 2024Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systemscited by 1
- 2026Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithmscited by 1
- 2026Continuous Data Assimilation with Learned Surrogate Dynamicscited by 1
- 2026Energy-based Transport for Amortized Bayesian Inferencecited by 0
- 2026The interface of data assimilation and machine learningcited by 0
- 2026The interface of data assimilation and machine learning5 Oct 2026
- 2026Learning Probabilistic Filters with Strictly Proper Scoring Rules25 Jun 2026
- 2026Continuous Data Assimilation with Learned Surrogate Dynamics30 May 2026
- 2026Energy-based Transport for Amortized Bayesian Inference14 May 2026
- 2026Amortized Filtering and Smoothing with Conditional Normalizing Flows8 Apr 2026
- 2026Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms21 Mar 2026
- 2024Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems18 Dec 2024