KA-CRNNs learn pressure-dependent and collider-specific kinetic rate laws from data using Kolmogorov-Arnold activations inside a CRNN framework, outperforming interpolative methods by 2.88x in MSE on two proof-of-concept reactions.
hub
Forward-Mode Automatic Differentiation in Julia
15 Pith papers cite this work. Polarity classification is still indexing.
15
Pith papers citing it
abstract
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++. Unlike recently developed AD tools in other popular high-level languages such as Python and MATLAB, ForwardDiff takes advantage of just-in-time (JIT) compilation to transparently recompile AD-unaware user code, enabling efficient support for higher-order differentiation and differentiation using custom number types (including complex numbers). For gradient and Jacobian calculations, ForwardDiff provides a variant of vector-forward mode that avoids expensive heap allocation and makes better use of memory bandwidth than traditional vector mode. In our numerical experiments, we demonstrate that for nontrivially large dimensions, ForwardDiff's gradient computations can be faster than a reverse-mode implementation from the Python-based autograd package. We also illustrate how ForwardDiff is used effectively within JuMP, a modeling language for optimization. According to our usage statistics, 41 unique repositories on GitHub depend on ForwardDiff, with users from diverse fields such as astronomy, optimization, finite element analysis, and statistics. This document is an extended abstract that has been accepted for presentation at the AD2016 7th International Conference on Algorithmic Differentiation.
hub tools
citation-role summary
method 3
background 1
citation-polarity summary
representative citing papers
Develops an optimization framework for the linear response of SRB measures to perturbations of Anosov diffeomorphisms, proving uniqueness of the optimal perturbation for non-degenerate cases, giving explicit Fourier coefficients, and providing a convergent Fourier-based numerical scheme.
IIR shows that under a one-to-one componentwise transformation where observables depend on fixed linear combinations of transformed parameters, a single numerical Jacobian determines the lower-dimensional reparameterisation space.
The paper presents ModelPredictiveControl.jl, an open-source Julia toolkit for model predictive control including nonlinear, economic, and successive linearization variants, illustrated with CSTR and inverted pendulum simulations and benchmarked against MATLAB.
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.
A new stochastic differential dynamic programming method optimizes coupled trajectory design and orbit determination under partial observability, producing navigation-aware solutions with lower fuel consumption than deterministic local optimization in examples like the circular restricted three-body
An admissible Lax-Wendroff flux reconstruction method with automatic differentiation and subcell blending enables robust high-order simulations of relativistic hydrodynamics on adaptive curved meshes.
KA-CRNN learns continuous SOC-dependent kinetic parameters for cathode-electrolyte decomposition directly from DSC data, reproducing heat-release features across all SOCs for NCA, NM, and NMA cathodes.
A PINN learns higher-order corrections to the TaylorT4 PN model from eight NR surrogate waveforms, reducing phase and amplitude errors in the inspiral while enforcing physical symmetries.
AGMCTS augments MCTS with action-score gradients for particle beliefs, a Multiple Importance Sampling tree for reuse, and Area Formula gradients for smooth models, outperforming prior sample-based solvers on continuous benchmarks.
Constrained policy optimization for stochastic optimal control under nonstationary uncertainties via Markov embeddability and finite approximation.
Four finite volume schemes based on different flux formulations are proposed for a degenerated drift-diffusion system, with stability and existence shown for all four and convergence proven for two, plus numerical experiments.
A method for adjoint differentiation of stencil loops that preserves their structure and parallelizability via combined AD and loop transformations, released as the PerforAD tool with seismic and CFD test cases.
A five-parameter PCA model for n(z) uncertainties in Stage-IV 3x2-pt analyses degrades the S8 constraint by only 5% relative to shift-stretch models while halving biases on Omega_m and sigma_8, and all tested models allow safe analytical marginalization with speed-ups up to 25x.
Differentiable GRMHD image sensitivities create a structured error landscape that supports gradient-based parameter recovery for black hole imaging under idealized and noisy conditions.
citing papers explorer
-
Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws
KA-CRNNs learn pressure-dependent and collider-specific kinetic rate laws from data using Kolmogorov-Arnold activations inside a CRNN framework, outperforming interpolative methods by 2.88x in MSE on two proof-of-concept reactions.
-
Optimal linear response for Anosov diffeomorphisms
Develops an optimization framework for the linear response of SRB measures to perturbations of Anosov diffeomorphisms, proving uniqueness of the optimal perturbation for non-degenerate cases, giving explicit Fourier coefficients, and providing a convergent Fourier-based numerical scheme.
-
Invariant Image Reparameterisation: Bridging Symbolic and Numerical Methods for Identifiability Analysis and Model Reduction
IIR shows that under a one-to-one componentwise transformation where observables depend on fixed linear combinations of transformed parameters, a single numerical Jacobian determines the lower-dimensional reparameterisation space.
-
ModelPredictiveControl.jl: advanced process control made easy in Julia
The paper presents ModelPredictiveControl.jl, an open-source Julia toolkit for model predictive control including nonlinear, economic, and successive linearization variants, illustrated with CSTR and inverted pendulum simulations and benchmarked against MATLAB.
-
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.
-
Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability
A new stochastic differential dynamic programming method optimizes coupled trajectory design and orbit determination under partial observability, producing navigation-aware solutions with lower fuel consumption than deterministic local optimization in examples like the circular restricted three-body
-
Admissible Lax-Wendroff Flux Reconstruction Method with Automatic Differentiation on Adaptive Curved Meshes for Relativistic Hydrodynamics
An admissible Lax-Wendroff flux reconstruction method with automatic differentiation and subcell blending enables robust high-order simulations of relativistic hydrodynamics on adaptive curved meshes.
-
Learning continuous state of charge dependent thermal decomposition kinetics for Li-ion cathodes using Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs)
KA-CRNN learns continuous SOC-dependent kinetic parameters for cathode-electrolyte decomposition directly from DSC data, reproducing heat-release features across all SOCs for NCA, NM, and NMA cathodes.
-
Learning Post-Newtonian Corrections from Numerical Relativity
A PINN learns higher-order corrections to the TaylorT4 PN model from eight NR surrogate waveforms, reducing phase and amplitude errors in the inspiral while enforcing physical symmetries.
-
Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs
AGMCTS augments MCTS with action-score gradients for particle beliefs, a Multiple Importance Sampling tree for reuse, and Area Formula gradients for smooth models, outperforming prior sample-based solvers on continuous benchmarks.
-
Constrained Policy Optimization for Stochastic Optimal Control under Nonstationary Uncertainties
Constrained policy optimization for stochastic optimal control under nonstationary uncertainties via Markov embeddability and finite approximation.
-
A numerical analysis focused comparison of several Finite Volume schemes for an Unipolar Degenerated Drift-Diffusion Model
Four finite volume schemes based on different flux formulations are proposed for a degenerated drift-diffusion system, with stability and existence shown for all four and convergence proven for two, plus numerical experiments.
-
Automatic Differentiation for Adjoint Stencil Loops
A method for adjoint differentiation of stencil loops that preserves their structure and parallelizability via combined AD and loop transformations, released as the PerforAD tool with seismic and CFD test cases.
-
Propagating data-driven galaxy redshift distribution uncertainties in 3$\times$2-pt analyses
A five-parameter PCA model for n(z) uncertainties in Stage-IV 3x2-pt analyses degrades the S8 constraint by only 5% relative to shift-stretch models while halving biases on Omega_m and sigma_8, and all tested models allow safe analytical marginalization with speed-ups up to 25x.
-
Sensitivities of Black Hole Images from GRMHD Simulations
Differentiable GRMHD image sensitivities create a structured error landscape that supports gradient-based parameter recovery for black hole imaging under idealized and noisy conditions.