23 papers · ranked by Valyu relevance
Nick McGreivy
Differentiable programming allows for derivatives of functions implemented via computer code to be calculated automatically. These derivatives are calculated using automatic differentiation (AD). This thesis explores two applications of differentiable programming to computational plasma physics. First, we consider how…
Steven A. Frank
A computational revolution unleashed the power of articial neural networks. At the heart of that revolution is automatic dierentiation, which calculates the derivative of a performance measure relative to a large number of parameters. Dierentiation enhances the discovery of improved performance in large models, an…
Xipeng Shen, Guoqiang Zhang, Irene Dea, Samantha Andow + 10 more
'Emilio Arroyo-Fang' 'Neal Gafter' 'Johann George' 'Melissa Grueter' 'Erik Meijer' 'Olin Shivers' 'Steffi Stumpos' 'Alanna Tempest' 'Christy Warden' 'Shannon Yang'] This paper presents a novel optimization for differentiable programming named coarsening optimization. It offers a systematic way to synergize symbolic…
Shaoyi Yang
Thermodynamic and flash equilibrium calculations are the cornerstones of simulation process calculations. The iterative approach, a widely used nonlinear problem-solving technique, relies on derivative calculations throughout the procedure that directly affect the stability and effectiveness of the solution. In this…
Paige Bright, Alan Edelman, Steven G. Johnson
The first time that Professor Edelman had heard about automatic differentiation (AD), it was easy for him to imagine what it was . . . but what he imagined was wrong! In his head, he thought it was straightforward symbolic differentiation applied to code—sort of like executing Mathematica or Maple, or even just…
Francesco Guzzi, Alessandra Gianoncelli, Fulvio Billè, Sergio Carrato + 2 more
Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for advanced…
Marius Pille, Leon Martin, Emilius Richter, Dionysios Perdikis + 2 more
Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and…
R. Newbury, Jack Collins, Kerry He, Jiahe Pan + 3 more
Differentiable simulators continue to push the state of the art across a range of domains including computational physics, robotics, and machine learning. Their main value is the ability to compute gradients of physical processes, which allows differentiable simulators to be readily integrated into commonly employed…
Masoud Barati
Descent and Computational Graphs Authors: ['Masoud Barati'] Abstract—This paper presents a new method for enhancing Alternating Current Power Flow (ACPF) analysis. The method integrates the Newton-Raphson (NR) method with Enhanced-Gradient Descent (GD) and computational graphs. The integration of renewable energy…
Nils Gönnheimer, Karsten Reuter, Johannes T. Margraf
Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation Authors: ['Nils Gönnheimer' 'Karsten Reuter' 'Johannes T. Margraf'] The development of machine learning interatomic potentials (MLIPs) has revolutionized computational chemistry by enhancing the accuracy of…
Hend Dawood, Nefertiti Megahed, Nicholas Higham
Acquiring reliable knowledge amidst uncertainty is a topical issue of modern science. Interval mathematics has proved to be of central importance in coping with uncertainty and imprecision. Algorithmic differentiation, being superior to both numeric and symbolic differentiation, is nowadays one of the most celebrated…
Patryk Chaber, Bartosz Chaber, Francesc Pozo
Virtual sensing is an emerging field of research that has garnered increasing attention in recent years. In this paper, we focus our attention on recurrent neural networks for time-series forecasting, namely, the Nonlinear AutoRegressive eXogenous model (NARX). The NARX is utilized as a surrogate neural network for…
Hamza Farooq, Yongxin Chen, Ghulam Rasool, Tryphon T. Georgiou + 1 more
Accurate characterization of the brain microstructure using diffusion MRI (dMRI) relies on optimal (i.e. information-rich) scanning protocols. Presently, without such protocols, extensive datasets are necessary to image the intricate microarchitecture of the brain by fitting complex biophysical models to the data. This…
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange
Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to…
Sebastian Persson, Fabian Fröhlich, Stephan Grein, Torkel Loman + 4 more
Dynamic models are useful to study processes ranging from cell signalling to cell differentiation. Common modelling workflows such as model exploration and parameter estimation are computationally demanding. The Julia programming language is a promising tool to address these computational challenges. To evaluate it we…
Benjamin Lieser, Georgy Belousov, Johannes Söding
Background Most popular tools for reconstructing phylogenetic trees from multiple sequence alignments use a model of molecular evolution in which a single substitution matrix or a small set of fixed matrices are shared between all columns. Models with column-specific rate matrices can in principle be fit by automatic…
Erica C. Mitchell, Justin M. Turney, Henry F. Schaefer III
for Explicitly Correlated MP2 Authors: ['Erica\nC. Mitchell' 'Justin M. Turney' 'Henry F. Schaefer III'] Automatic differentiation (AD) offers a route to achieve arbitrary-order derivatives of challenging wave function methods without the use of analytic gradients or response theory. Currently, AD has been…
Xinyan Wang, Jichen Li, Lan Yang, Feiyang Chen + 5 more
In the simulation of molecular systems, the underlying force field (FF) model plays an extremely important role, determining the reliability of the simulation. However, the quality of the state-of-the-art molecular force fields is still unsatisfactory in many cases, and the FF parameterization process largely relies on…
Ulrich K. Deiters, Ian H. Bell
1## Introduction Differentiation of functions plays a major role in many disciplines of science, from physics to engineering. A special case is thermodynamics, which is practically built upon differentiation: thermodynamic theories and models usually represent observable properties of matter as derivatives of some…
Authors not listed
The modeling of chemical reactions using artificial intelligence is rapidly advancing but still heavily relies on abundant and costly experimental data. In the context of computational chemistry, we present SymChemAI, a chemically informed neural network capable of simulating the dynamic evolution of reactions from…
Authors not listed
This Perspective is focused on Permutationally invariant polynomials (PIPs). Since their introduction in 2004 and first use in developing a fully permutationally invariant potential for the highly fluxional cation CH+5 , PIPs have found widespread use in developing machine learned potentials (MLPs) for isolated…
Hiroshi Nakano, Shinnosuke Hattori, Hajime Kobayashi, Takumi Araki + 3 more
In this paper, we present a new force field (FF) parameterization method with direct matching of crystal structures and atomic charges optimization in end-to-end differentiable manner. The advancement of force field (FF) parameterization methods has been accelerated by differentiable programming. Automatic…
Ali Poursina, Shayan Hajhashemi, Arsham Mikaeili Namini, Ali Saberi + 2 more
Inferring the governing dynamics of differentiation that capture cell state evolution remains a central challenge in single-cell biology. We present Latent Space Dynamics (LSD), a thermodynamics-inspired framework that models cell differentiation as evolution on a learned Waddington landscape in latent space. LSD…