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A. Griewank, T. Streubel, C. Tischendorf
On the abs-polynomial expansion of piecewise smooth functions
Article in Optimization Methods & Software, Taylor & Francis, 2021
Theory & Techniques:
Piecewise Linear
Saeed Azad, Michael J. Alexander-Ramos
Robust Combined Design and Control Optimization of Hybrid-Electric Vehicles Using MDSDO
Article in IEEE Transactions on Vehicular Technology, 2021
Application Area:
Electric Vehicle Powertrain
Tools:
ADiMat
Hong Zhang, Emil Constantinescu
Revolve-Based Adjoint Checkpointing for Multistage Time Integration
Conference proceeding, Computational Science -- ICCS 2021, Springer International Publishing, 2021
Theory & Techniques:
Checkpointing
Rebecca Hyen Hae Kim
Dynamic Optimization Algorithms for Baseload Power Plant Cycling under Variable Renewable Energy
Ph.D. thesis, Department of Chemical and Biomedical Engineering, West Virginia University, 2021
Application Area:
Chemistry, Dynamic Optimization
Tools:
ADiMat
N. McGreivy, S. R. Hudson, C. Zhu
Optimized finite-build stellarator coils using automatic differentiation
Article in Nuclear Fusion, IOP Publishing, 2021
Application Area:
Nuclear Fusion
Tools:
JAX
Adam S. Abbott, Boyi Z. Abbott, Justin M. Turney, Henry F. Schaefer
Arbitrary-Order Derivatives of Quantum Chemical Methods via Automatic Differentiation
Article in The Journal of Physical Chemistry Letters, American Chemical Society (ACS), 2021
Application Area:
Chemistry
Tools:
JAX
Dominik Garmatter, Andrea Maggi, Marcus Wenzel, Shaimaa Monem, Mirko Hahn, Martin Stoll, Sebastian Sager, Peter Benner, Kai Sundmacher
Power-to-Chemicals: A Superstructure Problem for Sustainable Syngas Production
Mathematical Modeling, Simulation and Optimization for Power Engineering and Management, Springer International Publishing, 2021
Application Area:
Chemistry
Tools:
ADiMat
Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis
DeepXDE: A Deep Learning Library for Solving Differential Equations
Article in SIAM Review, 2021
Application Area:
Machine Learning
Tim Kaler, Tao B. Schardl, Brian Xie, Charles E. Leiserson, Jie Chen, Aldo Pareja, Georgios Kollias
PARAD: A Work-Efficient Parallel Algorithm for Reverse-Mode Automatic Differentiation
Conference proceeding, Proceedings of the Symposium on Algorithmic Principles of Computer Systems (APOCS), Society for Industrial and Applied Mathematics, 2021
Theory & Techniques:
Parallelism, Reverse Mode
Belmiro P. M. Duarte, Anthony C. Atkinson, José F. O. Granjo, Nuno M. C. Oliveira
A model-based framework assisting the design of vapor-liquid equilibrium experimental plans
Article in Computers & Chemical Engineering, 2021
Application Area:
Chemistry
Tools:
ADiMat
Jorge López, Cosmin Anitescu, Timon Rabczuk
Isogeometric structural shape optimization using automatic sensitivity analysis
Article in Applied Mathematical Modelling, 2021
Application Area:
Shape optimization
Tools:
ADiMat
Sören Laue, Matthias Mitterreiter, Joachim Giesen
A Simple and Efficient Tensor Calculus
Conference proceeding, The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7--12, 2020, AAAI Press, 2020
Theory & Techniques:
Hierarchical Approach
Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos
Derivatives and Automatic Differentiation
Machine Learning Refined: Foundations, Algorithms, and Applications, Cambridge University Press, 2020
Theory & Techniques:
Introduction
Jan Hückelheim, Michel Schanen, Sri Hari Krishna Narayanan, Paul Hovland
Vector Forward Mode Automatic Differentiation on SIMD/SIMT Architectures
Conference proceeding, 49th International Conference on Parallel Processing -- ICPP, Association for Computing Machinery, 2020
Theory & Techniques:
Parallelism
R. Seidler
Automatisierte Anbindung von Simulations- und Optimierungssoftware zur parallelen Lösung inverser Problemklassen
Ph.D. thesis, Department of Mathematics and Computer Science, Friedrich Schiller University Jena, 2020
Application Area:
Inverse Problems, Optimization
Tools:
TAPENADE
G. D. Balogh, I. Z. Reguly
Automatic parallel implementations of adjoint codes for structured mesh applications
Conference proceeding, 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), IEEE, 2020
Theory & Techniques:
Toolkits
P. Nobel
auto_diff: An Automatic Differentiation Package For Python
Conference proceeding, 2020 Spring Simulation Conference (SpringSim), Society for Modeling and Simulation International (SCS), 2020
Tools:
auto_diff
William S. Moses, Valentin Churavy
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
Advances in Neural Information Processing Systems 33, 2020
Theory & Techniques:
Adjoint, control-flow reversal, Fixpoint, General, Static Analysis
Awad H. Al-Mohy, Bahar Arslan
The complex step approximation to the higher order Fréchet derivatives of a matrix function
Article in Numerical Algorithms, Springer Science and Business Media LLC, 2020
Theory & Techniques:
Complex Step Differentiation, Higher Order
Assefaw H. Gebremedhin, Andrea Walther
An introduction to algorithmic differentiation
Article in WIREs Data Mining and Knowledge Discovery, 2020
Theory & Techniques:
Introduction

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