A graph-based methodology for constructing computational models that automates adjoint-based sensitivity analysis
V Gandarillas, AJ Joshy, MZ Sperry, AK Ivanov… - Structural and …, 2024 - Springer
The adjoint method provides an efficient way to compute sensitivities for system models with
a large number of inputs. However, implementing the adjoint method requires significant …
a large number of inputs. However, implementing the adjoint method requires significant …
MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade
SS Gaikwad, SHK Narayanan, L Hascoet… - Future Generation …, 2024 - Elsevier
Abstract The Massachusetts Institute of Technology General Circulation Model (MITgcm) is
widely used by the climate science community to simulate planetary atmosphere and ocean …
widely used by the climate science community to simulate planetary atmosphere and ocean …
A taxonomy of automatic differentiation pitfalls
Automatic differentiation is a popular technique for computing derivatives of computer
programs. While automatic differentiation has been successfully used in countless …
programs. While automatic differentiation has been successfully used in countless …
Slang. d: Fast, modular and differentiable shader programming
We introduce SLANG. D, an extension to the Slang shading language that incorporates first-
class automatic differentiation support. The new shading language allows us to transform a …
class automatic differentiation support. The new shading language allows us to transform a …
Differentiable solver for time-dependent deformation problems with contact
We introduce a general differentiable solver for time-dependent deformation problems with
contact and friction. Our approach uses a finite element discretization with a high-order time …
contact and friction. Our approach uses a finite element discretization with a high-order time …
Automatic Differentiation for Explicitly Correlated MP2
EC Mitchell, JM Turney… - Journal of Chemical …, 2024 - ACS Publications
Automatic differentiation (AD) offers a route to achieve arbitrary-order derivatives of
challenging wave function methods without the use of analytic gradients or response theory …
challenging wave function methods without the use of analytic gradients or response theory …
Understanding Automatic Differentiation Pitfalls
Automatic differentiation, also known as backpropagation, AD, autodiff, or algorithmic
differentiation, is a popular technique for computing derivatives of computer programs …
differentiation, is a popular technique for computing derivatives of computer programs …
Automatic adjoint-based inversion schemes for geodynamics: reconstructing the evolution of Earth's mantle in space and time
S Ghelichkhan, A Gibson, DR Davies… - Geoscientific Model …, 2024 - gmd.copernicus.org
Reconstructing the thermo-chemical evolution of Earth's mantle and its diverse surface
manifestations is a widely recognised grand challenge for the geosciences. It requires the …
manifestations is a widely recognised grand challenge for the geosciences. It requires the …
JuliQAOA: Fast, Flexible QAOA Simulation
We introduce JuliQAOA, a simulation package specifically built for the Quantum Alternating
Operator Ansatz (QAOA). JuliQAOA does not require a circuit-level description of QAOA …
Operator Ansatz (QAOA). JuliQAOA does not require a circuit-level description of QAOA …
Transparent Checkpointing for Automatic Differentiation of Program Loops Through Expression Transformations
Automatic differentiation (AutoDiff) in machine learning is largely restricted to expressions
used for neural networks (NN), with the depth rarely exceeding a few tens of layers …
used for neural networks (NN), with the depth rarely exceeding a few tens of layers …