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Automatic Differentiable Procedural Modeling
Computer Graphics Forum, 2022AbstractProcedural modeling allows for an automatic generation of large amounts of similar assets, but there is limited control over the generated output. We address this problem by introducing Automatic Differentiable Procedural Modeling (ADPM). The forward procedural model generates a final editable model.
Mathieu Gaillard +4 more
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Automatic differentiation of quadrature
Optimization Methods and Software, 2012We analyse the application of automatic differentiation (AD) to the quadrature (numerical integration) of a function integrand to determine the sensitivities of the integral to variations in the limits of integration. We derive an expression for the truncation errors of such AD-derived sensitivities and relate them to the truncation error of the ...
Marina Menshikova, Shaun A. Forth
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On automatic differentiation for the Matérn covariance
CoRR, 2022To target challenges in differentiable optimization we analyze and propose strategies for derivatives of the Mat\'ern kernel with respect to the smoothness parameter. This problem is of high interest in Gaussian processes modelling due to the lack of robust derivatives of the modified Bessel function of second kind with respect to order. In the current
Oana Marin +2 more
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Automatic Differentiation in ACL2
2011In this paper, we describe recent improvements to the theory of differentiation that is formalized in ACL2(r). First, we show how the normal rules for the differentiation of composite functions can be introduced in ACL2(r). More important, we show how the application of these rules can be largely automated, so that ACL2(r) can automatically define the ...
Peter Reid, Ruben Gamboa
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Stochastic automatic differentiation: automatic differentiation for Monte-Carlo simulations
Quantitative Finance, 2017In this paper we re-formulate the automatic differentiation (and in particular, the backward automatic differentiation, also known as adjoint automatic differentiation, AAD) for random variables.
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Automatic Differentiation of Computer Programs
ACM Transactions on Mathematical Software, 1980Abstract : A method for the automatic differentiation of computer functions (subroutines) written in a high level language is discussed. A theory is developed to show that most functions that arise in applications can be differentiated automatically. It is shown how one can take FORTRAN function (Subroutine) and, with the aid of a precompiler, obtain a
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Exploiting parallelism in automatic differentiation
Proceedings of the 5th international conference on Supercomputing - ICS '91, 1991The numerical methods employed in the solution of many scientific computing problems require the computation of first or second order derivatives of a function f:R{sup n} {yields} R{sup m}. We present an approach that, given a serial C program for the computation of f(x), derives a parallel execution schedule for the computation of f and its ...
Christian H. Bischof +2 more
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Automatic differentiation in robust optimization
AIAA Journal, 1996The paper deals with automatic differentiation techniques to avoid the costly finite differencing of robust objective functions and robust constraints. Sensitivities calculated by automatic differentiation are exact and therefore enhance performance.
Su, J., Renaud, J. E.
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ADP: Automatic differentiation ptychography
2018 IEEE International Conference on Computational Photography (ICCP), 2018Ptychography is an imaging technique which aims to recover the complex-valued exit wavefront of an object from a set of its diffraction pattern magnitudes. Ptychography is one of the most popular techniques for sub-30 nanometer imaging as it does not suffer from the limitations of typical lens based imaging techniques.
Sushobhan Ghosh +3 more
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Source-to-Source Automatic Differentiation of OpenMP Parallel Loops
ACM Transactions on Mathematical Software, 2022Jan Hückelheim
exaly