DiffSharp

DiffSharp: Automatic differentiation library. DiffSharp is a functional automatic differentiation (AD) library. AD allows exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution. AD is different from numerical differentiation, which is prone to truncation and round-off errors, and symbolic differentiation, which is affected by expression swell and cannot fully handle algorithmic control flow. Using the DiffSharp library, differentiation (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) is applied using higher-order functions, that is, functions which take other functions as arguments. Your functions can use the full expressive capability of the language including control flow. DiffSharp allows composition of differentiation using nested forward and reverse AD up to any level, meaning that you can compute exact higher-order derivatives or differentiate functions that are internally making use of differentiation. Please see the API Overview page for a list of available operations. The library is developed by Atılım Güneş Baydin and Barak A. Pearlmutter mainly for research applications in machine learning, as part of their work at the Brain and Computation Lab, Hamilton Institute, National University of Ireland Maynooth. DiffSharp is implemented in the F# language and can be used from C# and the other languages running on Mono, .NET Core, or the .Net Framework, targeting the 64 bit platform. It is tested on Linux and Windows. We are working on interfaces/ports to other languages.


References in zbMATH (referenced in 60 articles )

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  1. Amini Niaki, Sina; Haghighat, Ehsan; Campbell, Trevor; Poursartip, Anoush; Vaziri, Reza: Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (2021)
  2. Angeli, Andrea; Desmet, Wim; Naets, Frank: Deep learning for model order reduction of multibody systems to minimal coordinates (2021)
  3. Bolte, Jérôme; Pauwels, Edouard: Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning (2021)
  4. Coope, Ian D.; Tappenden, Rachael: Gradient and diagonal Hessian approximations using quadratic interpolation models and aligned regular bases (2021)
  5. Dong, Suchuan; Li, Zongwei: Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations (2021)
  6. Falcone, Alberto; Garro, Alfredo; Mukhametzhanov, Marat S.; Sergeyev, Yaroslav D.: A Simulink-based software solution using the infinity computer methodology for higher order differentiation (2021)
  7. Flaschel, Moritz; Kumar, Siddhant; De Lorenzis, Laura: Unsupervised discovery of interpretable hyperelastic constitutive laws (2021)
  8. Ghadai, Sambit; Lee, Xian Yeow; Balu, Aditya; Sarkar, Soumik; Krishnamurthy, Adarsh: Multi-resolution 3D CNN for learning multi-scale spatial features in CAD models (2021)
  9. Haghighat, Ehsan; Bekar, Ali Can; Madenci, Erdogan; Juanes, Ruben: A nonlocal physics-informed deep learning framework using the peridynamic differential operator (2021)
  10. Haghighat, Ehsan; Juanes, Ruben: SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (2021)
  11. Haghighat, Ehsan; Raissi, Maziar; Moure, Adrian; Gomez, Hector; Juanes, Ruben: A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (2021)
  12. Iavernaro, F.; Mazzia, F.; Mukhametzhanov, M. S.; Sergeyev, Ya. D.: Computation of higher order Lie derivatives on the infinity computer (2021)
  13. Itzá Balam, Reymundo; Hernandez-Lopez, Francisco; Trejo-Sánchez, Joel; Uh Zapata, Miguel: An immersed boundary neural network for solving elliptic equations with singular forces on arbitrary domains (2021)
  14. Lee, Jae Yong; Jang, Jin Woo; Hwang, Hyung Ju: The model reduction of the Vlasov-Poisson-Fokker-Planck system to the Poisson-Nernst-Planck system via the deep neural network approach (2021)
  15. Lu, Lu; Meng, Xuhui; Mao, Zhiping; Karniadakis, George Em: DeepXDE: a deep learning library for solving differential equations (2021)
  16. Pauwels, Edouard: Incremental without replacement sampling in nonconvex optimization (2021)
  17. Pyrialakos, Stefanos; Kalogeris, Ioannis; Sotiropoulos, Gerasimos; Papadopoulos, Vissarion: A neural network-aided Bayesian identification framework for multiscale modeling of nanocomposites (2021)
  18. Ranade, Rishikesh; Hill, Chris; Pathak, Jay: Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (2021)
  19. Schoenholz, Samuel S.; Cubuk, Ekin D.: JAX, M.D. a framework for differentiable physics (2021)
  20. Wang, Li; Yan, Zhenya: Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (2021)

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