Universal Differential Equations unify scientific models with machine learning by embedding flexible approximators into differential equations, enabling applications from biological mechanism discovery to high-dimensional optimization.
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Zygote is a differentiable programming system in Julia that supports gradients for nearly all language constructs while generating high-performance code without user refactoring.
Review of gradient-based and gradient-free methods for parameter point estimation plus profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification in systems biology models.
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Universal Differential Equations for Scientific Machine Learning
Universal Differential Equations unify scientific models with machine learning by embedding flexible approximators into differential equations, enabling applications from biological mechanism discovery to high-dimensional optimization.
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A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Zygote is a differentiable programming system in Julia that supports gradients for nearly all language constructs while generating high-performance code without user refactoring.
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Parameter Estimation and Uncertainty Quantification for Systems Biology Models
Review of gradient-based and gradient-free methods for parameter point estimation plus profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification in systems biology models.