2 citations · 5 across the 12 of their papers we have counts for
15 papers
Developing a Numerical Algorithm with CIVL Model Checking in the Loop
Youngjun Lee, Anshu Dubey, Jan Hückelheim
Verifying a numerical algorithm in a large scientific simulation framework is challenging: the framework is too big to model-check, and the unit tests exercise only sampled inputs.…
Verification of PETSc with CIVL using LLM-generated ACSL contracts and deterministic driver generation
Hansol Suh, Jan Hückelheim, Stephen Siegel
Parallel numerical libraries such as PETSc are widely used in science and engineering applications where wrong results can have costly consequences. Despite this, numerical librari…
An Evaluation of Vertex Elimination Strategies for Algorithmic Differentiation
Alex Crane, Pål Grønås Drange, Eli Friedman +6
The algorithmic differentiation (AD) of mathematical functions can be interpreted as a sequence of vertex eliminations in an underlying directed acyclic graph. The problem of deter…
Challenges with Differentiable Quantum Dynamics
Sri Hari Krishna Narayanan, Michael Perlin, Robert Lewis-Swan +4
Differentiable quantum dynamics require automatic differentiation of a complex-valued initial value problem, which numerically integrates a system of ordinary differential equation…
Differentiating Through Linear Solvers
Paul Hovland, Jan Hückelheim
Computer programs containing calls to linear solvers are a known challenge for automatic differentiation. Previous publications advise against differentiating through the low-level…
Profiling checkpointing schedules in adjoint ST-AD
Laurent Hascoët, Jean-Luc Bouchot, Shreyas Sunil Gaikwad +2
Checkpointing is a cornerstone of data-flow reversal in adjoint algorithmic differentiation. Checkpointing is a storage/recomputation trade-off that can be applied at different lev…
Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +6
Numerical models of the ocean and ice sheets are crucial for understanding and simulating the impact of greenhouse gases on the global climate. Oceanic processes affect phenomena s…
MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade
Shreyas Sunil Gaikwad, Sri Hari Krishna Narayanan, Laurent Hascoet +6
The Massachusetts Institute of Technology General Circulation Model (MITgcm) is widely used by the climate science community to simulate planetary atmosphere and ocean circulations…
Forward Gradients for Data-Driven CFD Wall Modeling
Jan Hückelheim, Tadbhagya Kumar, Krishnan Raghavan +1
Computational Fluid Dynamics (CFD) is used in the design and optimization of gas turbines and many other industrial/ scientific applications. However, the practical use is often li…
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +5
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurrica…
Understanding Automatic Differentiation Pitfalls
Jan Hückelheim, Harshitha Menon, William Moses +3
Automatic differentiation, also known as backpropagation, AD, autodiff, or algorithmic differentiation, is a popular technique for computing derivatives of computer programs accura…
Model Checking Race-freedom When "Sequential Consistency for Data-race-free Programs" is Guaranteed
Wenhao Wu, Jan Hückelheim, Paul D. Hovland +2
Many parallel programming models guarantee that if all sequentially consistent (SC) executions of a program are free of data races, then all executions of the program will appear t…
Reducing Memory Requirements of Quantum Optimal Control
Sri Hari Krishna Narayanan, Thomas Propson, Marcelo Bongarti +2
Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE, which suffer from exponential growth in storage with increasing number of qubits a…
Combining Checkpointing and Data Compression to Accelerate Adjoint-Based Optimization Problems
Navjot Kukreja, Jan Hueckelheim, Mathias Louboutin +3
Seismic inversion and imaging are adjoint-based optimization problems that process up to terabytes of data, regularly exceeding the memory capacity of available computers. Data com…
Optimised finite difference computation from symbolic equations
Michael Lange, Navjot Kukreja, Fabio Luporini +4
Domain-specific high-productivity environments are playing an increasingly important role in scientific computing due to the levels of abstraction and automation they provide. In t…