Systematizing cellular complexity: A Hilbertian approach to biological problems
N Dehghani - PLOS Complex Systems, 2024 - journals.plos.org
Examining individual components of cellular systems has been successful in uncovering
molecular reactions and interactions. However, the challenge lies in integrating these …
molecular reactions and interactions. However, the challenge lies in integrating these …
Interpretable structural model error discovery from sparse assimilation increments using spectral bias‐reduced neural networks: A quasi‐geostrophic turbulence test …
Earth system models suffer from various structural and parametric errors in their
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …
Deep learning and symbolic regression for discovering parametric equations
Symbolic regression is a machine learning technique that can learn the equations governing
data and thus has the potential to transform scientific discovery. However, symbolic …
data and thus has the potential to transform scientific discovery. However, symbolic …
DISCOVER: Deep identification of symbolically concise open-form partial differential equations via enhanced reinforcement learning
The working mechanisms of complex natural systems tend to abide by concise partial
differential equations (PDEs). Methods that directly mine equations from data are called PDE …
differential equations (PDEs). Methods that directly mine equations from data are called PDE …
Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations
Despite rapid progress in data acquisition techniques, many complex physical, chemical,
and biological systems remain only partially observable, thus posing the challenge to …
and biological systems remain only partially observable, thus posing the challenge to …
Discovering conservation laws using optimal transport and manifold learning
Conservation laws are key theoretical and practical tools for understanding, characterizing,
and modeling nonlinear dynamical systems. However, for many complex systems, the …
and modeling nonlinear dynamical systems. However, for many complex systems, the …
Data-driven discovery of linear dynamical systems from noisy data
In modern science and engineering disciplines, data-driven discovery methods play a
fundamental role in system modeling, as data serve as the external representations of the …
fundamental role in system modeling, as data serve as the external representations of the …