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Fri Jul 28 12:00 PM -- 08:00 PM (PDT) @ Meeting Room 320 None
The Synergy of Scientific and Machine Learning Modelling (SynS & ML) Workshop
Antoine Wehenkel · Jörn Jacobsen · Emily Fox · Anuj Karpatne · Victoriya Kashtanova · Xuan Di · Emmanuel de Bézenac · Naoya Takeishi · Gilles Louppe

Workshop Home Page

The Synergy of Scientific and Machine Learning Modeling Workshop (“SynS & ML”) is an interdisciplinary forum for researchers and practitioners interested in the challenges of combining scientific and machine-learning models. The goal of the workshop is to gather together machine learning researchers eager to include scientific models into their pipelines, domain experts working on augmenting their scientific models with machine learning, and researchers looking for opportunities to incorporate ML in widely-used scientific models.

The power of machine learning (ML), its ability to build models by leveraging real-world data is also a big limitation; the quality and quantity of training data bound the validity domain of ML models. On the other hand, expert models are designed from first principles or experiences and labelled scientific if validated on curated real-world data, often even harvested for this specific purpose, as advised by the scientific method since Galileo. Expert models only describe idealized versions of the world which may hinder their deployment for important tasks such as accurate forecasting or parameter inference. This workshop focuses on the combination of two modelling paradigms: scientific and ML modelling. Sometimes called hybrid learning or grey-box modelling, this combination should 1) unlock new applications for expert models, and 2) leverage the data compressed within scientific models to improve the quality of modern ML models. In this spirit, the workshop focuses on the symbiosis between these two complementary modelling approaches; it aims to be a “rendezvous” between the involved communities, spanning sub-fields of science, engineering and health, and encompassing ML, to allow them to present their respective problems and solutions and foster new collaborations. The workshop invites researchers to contribute to such topics; see Call for Papers and Call for Scientific Models for more details.

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics - Rianne van den Berg (Talk)
The Domain Generalization Issue in Data-Based Dynamical Models - Patrick Gallinari (Talk)
Coffee Break (Break)
Climate modeling with AI: Hype or Reality? - Laure Zanna (Talk)
Poster Session 1 (Poster Session)
Lunch (Break)
AI-Augmented Epidemiology for Covid-19 - Sercan Arik (Talk)
Underspecification, inductive bias, and hybrid modeling - Andrew C. Miller (Talk)
ADEPT - Automatic Differentiation Enabled Plasma Transport (Spotlight)
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry (Spotlight)
Repurposing Density Functional Theory to Suit Deep Learning (Spotlight)
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback (Spotlight)
Coffee Break (Break)
ClimaX: A Foundation Model for Weather and Climate (Spotlight)
Titanium 3D Microstructure for Physics-based Generative Models: A Dataset and Primer (Spotlight)
Poster Session 2 (Poster Session)
Multi-Objective PSO-PINN (Poster)
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure (Poster)
Learning to Optimize Non-Convex Sum-Rate Maximization Problems (Poster)
Physics-Informed Neural Operator for Coupled Forward-Backward Partial Differential Equations (Poster)
Improving the Lipschitz stability in Spectral Transformer through Nearest Neighbour Coupling (Poster)
A language-based recommendation system for material discovery (Poster)
How to Select Physics-Informed Neural Networks in the Absence of Ground Truth: A Pareto Front-Based Strategy (Poster)
Estimation of Physical Coefficients for CO$_2$ Sequestration using Deep Generative Priors based Inverse Modeling Framework (Poster)
NuCLR: Nuclear Co-Learned Representations (Poster)
Coupling Self-Attention Generative Adversarial Network and Bayesian Inversion for Carbon Storage System (Poster)
How important are specialized transforms in Neural Operators? (Poster)
CAAFE: Combining Large Language Models with Tabular Predictors for Semi-Automated Data Science (Poster)
Adaptive Bias Correction for Improved Subseasonal Forecasting (Poster)
Understanding Energy-Based Modeling of Proteins via an Empirically Motivated Minimal Ground Truth Model (Poster)
Diffusion model based data generation for partial differential equations (Poster)
INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations (Poster)
Synergizing Deep Reinforcement Learning and Biological Pursuit Behavioral Rule for Robust and Interpretable Navigation (Poster)
Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic model (Poster)
Task-Linear Deep Representation of Physical Systems (Poster)
Exploring the Existence of Atmospheric Blocking’s Precursor Patterns with Physics-Informed Explainable AI (Poster)
Unbinned Profiled Unfolding (Poster)
Using machine learning and 3D geophysical modelling for mineral exploration (Poster)
Physics-Constrained Random Forests for Turbulence Model Uncertainty Estimation (Poster)
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling (Poster)
Infinite-Fidelity Surrogate Learning via High-order Gaussian Processes (Poster)
Predicting the stabilization quantity with neural networks for Singularly Perturbed Partial Differential Equations (Poster)
Reinstating Continuous Climate Patterns From Small and Discretized Data (Poster)
A Machine Learning Pressure Emulator for Hydrogen Embrittlement (Poster)
OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures (Poster)
Optimization or Architecture: What Matters in Non-Linear Filtering? (Poster)
Predictive Modeling of Engine-out Emissions using a Combination of Computational Fluid Dynamics and Machine Learning (Poster)
Accelerating Molecular Graph Neural Networks via Knowledge Distillation (Poster)
Good Lattice Accelerates Physics-Informed Neural Networks (Poster)
Neural Polytopes (Poster)
Neural Modulation Fields for Conditional Cone Beam Neural Tomography (Poster)
Learning Green's Function Efficiently Using Low-Rank Approximations (Poster)
Hybrid Diffusions for Stable Molecular Structure Generation via Explicit Energy-based Model (Poster)
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation (Poster)
Predicting Properties of Amorphous Solids with Graph Network Potentials (Poster)
Titanium 3D Microstructure for Physics-based Generative Models: A Dataset and Primer (Poster)
Speeding up Fourier Neural Operators via Mixed Precision (Poster)
Evaluating the diversity and utility of materials proposed by generative models (Poster)
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback (Poster)
Integrating processed-based models and machine learning for crop yield prediction (Poster)
Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics (Poster)
Simulation-based Inference with the Generalized Kullback-Leibler Divergence (Poster)
Repurposing Density Functional Theory to Suit Deep Learning (Poster)
Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization (Poster)
Convolutional Neural network for local stabilization parameter prediction for Singularly Perturbed PDEs (Poster)
Open Source Infrastructure for Differentiable Density Functional Theory (Poster)
An $\mathcal{A}$-adaptive Loop Unrolled Architecture for Solving Inverse Problems with Forward Model Mismatch (Poster)
Meta-Learning Deep Kernels for Latent Force Inference (Poster)
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context? (Poster)
RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows (Poster)
ClimaX: A Foundation Model for Weather and Climate (Poster)
Knowledge-Guided Additive Modeling For Supervised Regression (Poster)