Differentiable Scientific Programming
Adjoint sensitivity, automatic differentiation through ODE/PDE solvers.
Differentiable Scientific Programming. Adjoint sensitivity, automatic differentiation through ODE/PDE solvers.
Recent technical contributions
A handful of recent papers carry the methodological frontier of differentiable scientific programming forward. Automatic differentiation in machine learning: a survey (Baydin et al., 2018) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Supporting and adjacent work
A number of supporting contributions sharpen specific aspects of differentiable scientific programming or connect it to neighbouring problems. JAX: composable transformations of Python+NumPy programs (Bradbury, 2018) contributes to this area as one of the supporting references that inform current practice.
Open methodological questions for differentiable scientific programming include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- paper · primary · 2018Automatic differentiation in machine learning: a surveybaydin-2018, pearlmutter-2018, radul-2018, siskind-2018
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