438 citations · 508 across the 12 of their papers we have counts for
5 papers · 1 filter
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli +6
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. Whil…
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji +14
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remai…
Graph Neural Networks for IceCube Signal Classification
Nicholas Choma, Federico Monti, Lisa Gerhardt +7
Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly develop…
Optimizing the Union of Intersections LASSO () and Vector Autoregressive () Algorithms for Improved Statistical Estimation at Scale
Mahesh Balasubramanian, Trevor Ruiz, Brandon Cook +4
The analysis of scientific data of increasing size and complexity requires statistical machine learning methods that are both interpretable and predictive. Union of Intersections (…
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji +12
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which all…