Description
Chairperson: Xin Liu, JSC
The spatial and temporal resolution of Partial Differential Equations (PDEs) plays important roles in the mathematical description of the world’s physical phenomena. In general, scientists and engineers numerically solve PDEs by the use of computationally demanding solvers. Recently, deep learning algorithms have emerged as a viable alternative for obtaining fast solutions for PDEs. Models are...
Today's scientific applications and advanced instruments are producing extremely large volumes of data everyday, so that error-controlled lossy compression has become a critical technique to the scientific data storage and management. Existing lossy scientific data compressors, however, are designed mainly based on error-control driven mechanism, which cannot be efficiently applied in the...
Super-resolution tools have been originally invented for image super-resolution but are also increasingly used for improving scientific simulations or data-storage. Examples range from cosmology to urban prediction. One particular network framework, physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs), has been shown to be a powerful tool for subfilter...
Performance tuning, software/hardware co-design, and job scheduling are among the many tasks that rely on models to predict application performance. We propose and evaluate low rank tensor decomposition for modeling application performance. We use tensors to represent regular grids that discretize the input and configuration domain of an application. Application execution times mapped within...
Federated Learning (FL) is proposed as a solution to collaboratively learn a shared model in massively distributed environments without sharing private data of the participating parties.
While taking advantage of edge resources to compute model updates from a massive number of clients, it may lead to security risks.
Selected clients for a training round get access to the global model in...
The new IMPROVE project at ANL is collecting and curating AI models for cancer and similar precision medicine problems. Comparing these models across a large configuration space of hyperparameters and data sets is a challenging problem. The IMPROVE team is building a scalable workflow suite to answer a range of questions that arise when attempting to run diverse models developed by different...
From the sensor to the laptop, from the telescope to the supercomputer, from the microscope to the database, scientific discovery is part of a connected digital continuum that is dynamic and fast. In this new digital continuum, Artificial intelligence (AI) is providing tremendous breakthroughs, making data analysis and automated responses possible across the digital continuum. Sage is a...
