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SCML2024

Oral Presentations

  • Learning Reduced Order Dynamics via Geometric Representations [paper]

    Imran Nasim (IBM), Melanie Weber (Harvard University)

  • Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO) [paper]

    Oded Ovadia (Tel Aviv University), Vivek Oommen (Brown University), Adar Kahana (Brown University), Ahmad Peyvan (Brown University), Eli Turkel (Tel Aviv University), George Em Karniadakis (Brown University)

  • Identifying Dynamic Regulation with Adversarial Surrogates [paper]

    Ron Teichner (Technion, Israel Institute of Technology), Naama Brenner (Technion, Israel Institute of Technology) and Ron Meir (Technion, Israel Institute of Technology)

  • A Variable Projection Method for Computational PDEs with Artificial Neural Networks [paper]

    Suchuan Dong (Purdue University)

  • Towards accurate modeling of dynamics for molecular crystals by scalable variational Gaussian processes [paper]

    Mikhail Tsitsvero (Hokkaido University), Andrey Lyalin (National Institute for Materials Science, Hokkaido University), Mingoo Jin (Hokkaido University)

  • Hybrid Modeling Approach Using Cloud Dynamics and Deep Learning for Short-term Solar Forecasting [paper]

    Jun Sasaki (Japan Weather Association), Kenji Utsunomiya (Japan Weather Association), Maki Okada (Japan Weather Association), Koji Yamaguchi (Japan Weather Association)

  • Neural Networks are Integrable [paper]

    Yucong Liu (Georgia Institute of Technology)

  • Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs [paper]

    Alvaro Fernandez (DESY, Universität Hamburg), Nicolás Mendoza (DESY), Armin Iske (Universität Hamburg), Andrey Yachmenev (DESY, Universität Hamburg), Jochen Küpper (DESY, Universität Hamburg)

Poster Presentations

  • Discovering Intrinsic Multi-Compartment Pharmacometric Models Using Physics Informed Neural Networks [paper]

    Imran Nasim (IBM, University of Surrey), Adam Nasim (Merck Group, University of Surrey)

  • Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems [paper]

    Imran Nasim (IBM), Joaõ Lucas de Sousa Almeida (IBM)

  • Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [paper]

    Aku Kammonen (KAUST), Lisi Liang (RWTH Aachen), Anamika Pandey (RWTH Aachen), Raúl Tempone (KAUST, RWTH Aachen)

  • Synthetic Asset Price Paths Generation Using Denoising Diffusion Probabilistic Model [paper]

    Shujie Liu (University of Waterloo), Justin W.L. Wan (University of Waterloo)

  • Efficient Groundwater Flow Modeling Using Deep Neural Operators [paper]

    Maria Luisa Taccari (University of Leeds), He Wang (University College London), Somdatta Goswami (Johns Hopkins University), Mario De Florio (Brown University), Jonathan Nuttall (Deltares), Xiaohui Chen (University of Leeds), Peter K. Jimack (University of Leeds)

  • A physics-informed neural network for coupled calcium dynamics in a cable neuron [paper]

    Zachary M. Miksis (Temple University), Gillian Queisser (Temple University)

  • Learning Hamiltonian dynamics Under Uncertainty via Symplectic Gaussian Processes [paper]

    Yusuke Tanaka (NTT)

  • SPIGAN: A Generative Adversarial Network Supervised by Sparse Identification to Learn Governing Equations from Scarce Data [paper]

    Yue Shen (The University of Tokyo), Chen Yu (The University of Tokyo)

  • Domain-Decomposed Physics-Informed Neural Network Prediction on Cartesian CFD Framework [paper]

    Takashi Misaka (National Institute of Advanced Industrial Science and Technology (AIST)), Yusuke Mizuno (National Institute of Advanced Industrial Science and Technology (AIST)), Shogo Nakasumi (National Institute of Advanced Industrial Science and TechnoTechnology (AIST)), Yoshiyuki Furukawa (National Institute of Advanced Industrial Science and Technology (AIST))

  • Sparse Representation of Koopman Operator [paper]

    Yuya Note (Kobe University), Takaharu Yaguchi (Kobe University), Toshiaki Omori (Kobe University)

  • Biologically plausible local synaptic learning rules implement CNNs and denoising autoencoders [paper]

    Masataka Konishi (Kwansei Gakuin University), Keiji Miura (Kwansei Gakuin University)

  • Toward Bayesian Deep Grey-box Modeling [paper]

    Naoya Takeishi (The University of Tokyo, RIKEN)

  • Synthetic label masks mapped on ocean satellite background for oil seepage detection [paper]
    Lionel Boillot (TotalEnergies), Frédérik Pivot (TotalEnergies), Félix Klein (TotalEnergies)

  • Physics Informed Neural Networks with Application in Computational Structural Mechanics [paper]

    Aryan Verma (The Ohio State University), Dineshkumar Harursampath (Indian Institute of Science), Rajnish Mallick (Thapar Institute of Engineering & Technology), Prasant Sahay (Indian Institute of Science), Krishna Kant Mishra (Indian Institute of Science)