Korg: Fitting, Model Atmosphere Interpolation, and Brackett Lines by Adam J. Wheeler, Andrew R. Casey, Matthew W. Abruzzo

“…Built-in functions to fit observed spectra via synthesis or equivalent widths make it easy to take advantage of Korg 's automatic differentiation. Comparison to a past analysis of 18 Sco shows that we obtain significantly reduced line-to-line abundance scatter with Korg . …”
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Differentiable McCormick relaxations by Khan, Kamil A., Watson, Harry Alexander James, Barton, Paul I

“…Gradients of the new differentiable relaxations may be computed efficiently using the standard forward or reverse modes of automatic differentiation. Extensions to differentiable relaxations of implicit functions and solutions of parametric ordinary differential equations are discussed. …”
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Generalized physics-informed learning through language-wide differentiable programming by Rackauckas, C, Edelman, A, Fischer, K, Innes, M, Saba, E, Shah, VB, Tebbutt, W

“…We describe a ∂P system that is able to take gradients of full Julia programs, making Automatic Differentiation a first class language feature and compatibility with deep learning pervasive. …”
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Supercharging Programming through Compiler Technology by Moses, William S.

“…This thesis will demonstrate this approach through several real-world and composable compilers that I built for a variety of domains including parallelism, automatic differentiation, scheduling, portability, program search, and tensor arithmetic. …”
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Algorithms & Systems for Differentiable Graphics Programming by Bangaru, Sai Praveen

“…We discuss how the user-centric focus of SLANG.D’s automatic differentiation system enables users to write large-scale differentiable graphics pipelines and re-use 1000s of lines of existing rendering infrastructure without sacrificing its performance.…”
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Minimal Repetition Dynamic Checkpointing Algorithm for Unsteady Adjoint Calculation by Wang, Qiqi, Moin, Parviz, Iaccarino, Gianluca

“…This algorithm also has significant advantage in automatic differentiation when the length of execution is variable.…”
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Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine by Singh, Om Prakash, Palaniappan, Ramaswamy, Malarvili, Mb.

“…Therefore, this paper reports a relatively simple signal processing algorithm for automatic differentiation of asthma and non-asthma. CO2 signals were recorded from 30 non-asthmatic and 43 asthmatic patients. …”

Wavefield solutions from machine learned functions constrained by the Helmholtz equation by Tariq Alkhalifah, Chao Song, Umair bin Waheed, Qi Hao

“…For an input given by a location in the model space, the network learns to predict the wavefield value at that location, and its partial derivatives using a concept referred to as automatic differentiation, to fit, in our case, a form of the Helmholtz equation. …”
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The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread by M. Hashemi, A.N. Vattikonda, V. Sip, M. Guye, F. Bartolomei, M.M. Woodman, V.K. Jirsa

“…To invert the individualized whole-brain model employed in this study, we use the recently developed algorithms known as No-U-Turn Sampler (NUTS) as well as Automatic Differentiation Variational Inference (ADVI). Our results indicate that NUTS and ADVI accurately estimate the degree of epileptogenicity of brain regions, therefore, the hypothetical brain areas responsible for the seizure initiation and propagation, while the convergence diagnostics and posterior behavior analysis validate the reliability of the estimations. …”
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A fast method for fitting integrated species distribution models by Elliot Dovers, Gordana C. Popovic, David I. Warton

“…We propose a fast new methodology for fitting integrated distribution models using presence/absence and presence‐only data, via a spatial random effects approach combined with automatic differentiation. We have written an R package (called scampr) for straightforward implementation of our approach. …”
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Capturing dynamical correlations using implicit neural representations by Sathya R. Chitturi, Zhurun Ji, Alexander N. Petsch, Cheng Peng, Zhantao Chen, Rajan Plumley, Mike Dunne, Sougata Mardanya, Sugata Chowdhury, Hongwei Chen, Arun Bansil, Adrian Feiguin, Alexander I. Kolesnikov, Dharmalingam Prabhakaran, Stephen M. Hayden, Daniel Ratner, Chunjing Jia, Youssef Nashed, Joshua J. Turner

“…We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.…”
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GWFAST: A Fisher Information Matrix Python Code for Third-generation Gravitational-wave Detectors by Francesco Iacovelli, Michele Mancarella, Stefano Foffa, Michele Maggiore

“…In particular, GWFAST includes the effects of the Earth’s motion during the evolution of the signal, supports parallel computation, and relies on automatic differentiation rather than on finite differences techniques, which makes possible the computation of derivatives with accuracy close to machine precision. …”
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Scalable algorithms for physics-informed neural and graph networks by Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George E. Karniadakis

“…Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). …”
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End-to-end differentiable blind tip reconstruction for noisy atomic force microscopy images by Yasuhiro Matsunaga, Sotaro Fuchigami, Tomonori Ogane, Shoji Takada

“…In the method, we introduce a loss function including a regularization term to prevent overfitting to noise, and the tip shape is optimized with automatic differentiation and backpropagations developed in deep learning frameworks. …”
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A Swiss army infinitesimal jackknife by Jordan, MI, Stephenson, William T.(William Thomas), Broderick, Tamara A

“…These theoretical results, together with modern automatic differentiation software, support the application of the infinitesimal jackknife to a wide variety of practical problems in machine learning, providing a “Swiss Army infinitesimal jackknife.” …”
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Probabilistic Programming with Programmable Variational Inference by Becker, McCoy R., Lew, Alexander K., Wang, Xiaoyan, Ghavami, Matin, Huot, Mathieu, Rinard, Martin C., Mansinghka, Vikash K.

“…Finally, we present an automatic differentiation algorithm that differentiates these variational objectives, yielding provably unbiased gradient estimators for use during optimization. …”
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Inexpensive uncertainty analysis for CFD applications by Ghate, D

“…</p> <p>A consistent methodology has been developed for the automatic generation of the linear and adjoint codes by selective use of automatic differentiation (AD) technique. The method has the advantage of keeping the linear and the adjoint codes in-sync with the changes in the underlying nonlinear fluid mechanic solver. …”

Efficient high-dimensional variational data assimilation with machine-learned reduced-order models by R. Maulik, V. Rao, J. Wang, G. Mengaldo, E. Constantinescu, B. Lusch, P. Balaprakash, I. Foster, R. Kotamarthi

“…Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. We demonstrate our approach by performing an emulator-assisted DA forecast of geopotential height. …”
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Differentiable programming for Earth system modeling by M. Gelbrecht, M. Gelbrecht, A. White, A. White, S. Bathiany, S. Bathiany, N. Boers, N. Boers, N. Boers

“…Here, we argue that making ESMs automatically differentiable has a huge potential to advance ESMs, especially with respect to these key shortcomings. …”
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Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks by Samuel Ortega, Martin Halicek, Himar Fabelo, Rafael Camacho, María de la Luz Plaza, Fred Godtliebsen, Gustavo M. Callicó, Baowei Fei

“…In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. …”
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