120 citations · 223 across the 24 of their papers we have counts for
24 papers
Enhancing Energy Efficiency in Scientific Workflows through CFD based PIVAEs
Ali Zahir, Ashiq Anjum, Mark Wilkinson +1
The growing complexity and scale of scientific workflows in high performance computing (HPC) environments have led to significant challenges in managing energy consumption without…
NL2GDS: LLM-aided interface for Open Source Chip Design
Max Eland, Jeyan Thiyagalingam, Dinesh Pamunuwa +1
The growing complexity of hardware design and the widening gap between high-level specifications and register-transfer level (RTL) implementation hinder rapid prototyping and syste…
Data-driven modeling of shock physics by physics-informed MeshGraphNets
S. Zhang, M. Mallon, M. Luo +4
High-resolution fluid simulations for plasma physics and astrophysics rely on Particle in cell (PIC) and hydrodynamic solvers (e.g., FLASH) to resolve shock dominated, multiscale p…
Autonomous battery research: Principles of heuristic operando experimentation
Emily Lu, Gabriel Perez, Peter Baker +21
Unravelling the complex processes governing battery degradation is critical to the energy transition, yet the efficacy of operando characterisation is severely constrained by a lac…
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam +28
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring d…
Emulating CO Line Radiative Transfer with Deep Learning
Shiqi Su, Frederik De Ceuster, Jaehoon Cha +5
Modelling carbon monoxide (CO) line radiation is computationally expensive for traditional numerical solvers, especially when applied to complex, three-dimensional stellar atmosphe…
Deep Learning Evidence for Global Optimality of Gerver's Sofa
Kuangdai Leng, Jia Bi, Jaehoon Cha +2
The Moving Sofa Problem, formally proposed by Leo Moser in 1966, seeks to determine the largest area of a two-dimensional shape that can navigate through an -shaped corridor wit…
Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data
Saiful Khan, Scott Jones, Benjamin Bach +7
We present a method to create storytelling visualization with time series data. Many personal decisions nowadays rely on access to dynamic data regularly, as we have seen during th…
MLCommons Cloud Masking Benchmark with Early Stopping
Varshitha Chennamsetti, Gregor von Laszewski, Ruochen Gu +6
In this paper, we report on work performed for the MLCommons Science Working Group on the cloud masking benchmark. MLCommons is a consortium that develops and maintains several sci…
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
Kuangdai Leng, Mallikarjun Shankar, Jeyan Thiyagalingam
Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w.r.t. coordinates of co…
Padding-free Convolution based on Preservation of Differential Characteristics of Kernels
Kuangdai Leng, Jeyan Thiyagalingam
Convolution is a fundamental operation in image processing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, howev…
On the Compatibility between Neural Networks and Partial Differential Equations for Physics-informed Learning
Kuangdai Leng, Jeyan Thiyagalingam
We shed light on a pitfall and an opportunity in physics-informed neural networks (PINNs). We prove that a multilayer perceptron (MLP) only with ReLU (Rectified Linear Unit) or ReL…
Improved Imaging by Invex Regularizers with Global Optima Guarantees
Samuel Pinilla, Tingting Mu, Neil Bourne +1
Image reconstruction enhanced by regularizers, e.g., to enforce sparsity, low rank or smoothness priors on images, has many successful applications in vision tasks such as computer…
Unified Graph Neural Network Force-field for the Periodic Table
Kamal Choudhary, Brian DeCost, Lily Major +3
Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of materials. MLFFs have hitherto largely been designed and fitt…
Affinity-VAE: incorporating prior knowledge in representation learning from scientific images
Marjan Famili, Jola Mirecka, Camila Rangel Smith +8
Learning compact and interpretable representations of data is a critical challenge in scientific image analysis. Here, we introduce Affinity-VAE, a generative model that enables us…
ESA-Ariel Data Challenge NeurIPS 2022: Inferring Physical Properties of Exoplanets From Next-Generation Telescopes
Kai Hou Yip, Ingo P. Waldmann, Quentin Changeat +11
The study of extra-solar planets, or simply, exoplanets, planets outside our own Solar System, is fundamentally a grand quest to understand our place in the Universe. Discoveries i…
Discovering the building blocks of dark matter halo density profiles with neural networks
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +3
The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network mod…
Disentangling Autoencoders (DAE)
Jaehoon Cha, Jeyan Thiyagalingam
Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of…
Scientific Machine Learning Benchmarks
Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox +1
The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datase…
Investigating Deep Learning Methods for Obtaining Photometric Redshift Estimations from Images
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam +2
Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed,…
Entropy-based Active Learning of Graph Neural Network Surrogate Models for Materials Properties
Johannes Allotey, Keith T. Butler, Jeyan Thiyagalingam
Graph neural networks, trained on experimental or calculated data are becoming an increasingly important tool in computational materials science. Networks, once trained, are able t…
Benchmarking and Scalability of Machine Learning Methods for Photometric Redshift Estimation
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam +2
Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift es…
Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data
Keith T. Butler, Manh Duc Le, Jeyarajan Thiyagalingam +1
Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' pe…
Deep learning insights into cosmological structure formation
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +2
The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a…