4 citations · 12 across the 14 of their papers we have counts for
14 papers
FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction
Yuqiu Liu, Jialin Song, Marissa Ramirez de Chanlatte +5
Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D…
Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials
Alex Morehead, Miruna Cretu, Antonia Panescu +14
General-purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimi…
Is Flow Matching Just Trajectory Replay for Sequential Data?
Soon Hoe Lim, Shizheng Lin, Michael W. Mahoney +1
Flow matching (FM) is increasingly used in scientific domains for time series generation and forecasting, where data often arise from underlying dynamical systems. However, it is n…
PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training
Shenghao Yang, Zhichao Wang, Oleg Balabanov +2
Matrix functions such as square root, inverse roots, and orthogonalization play a central role in preconditioned gradient methods for neural network training. This has motivated th…
FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems
N. Benjamin Erichson, Vinicius Mikuni, Dongwei Lyu +4
We introduce FLEX (FLow EXpert), a backbone architecture for generative modeling of spatio-temporal physical systems using diffusion models. FLEX operates in the residual space rat…
Removing Watermarks with Partial Regeneration using Semantic Information
Krti Tallam, John Kevin Cava, Caleb Geniesse +2
As AI-generated imagery becomes ubiquitous, invisible watermarks have emerged as a primary line of defense for copyright and provenance. The newest watermarking schemes embed seman…
Advancing data-driven broadband seismic wavefield simulation with multi-conditional diffusion model
Zhengfa Bi, Nori Nakata, Rie Nakata +3
Sparse distributions of seismic sensors and sources pose challenges for subsurface imaging, source characterization, and ground motion modeling. While large-N arrays have shown the…
Tuning Frequency Bias of State Space Models
Annan Yu, Dongwei Lyu, Soon Hoe Lim +2
State space models (SSMs) leverage linear, time-invariant (LTI) systems to effectively learn sequences with long-range dependencies. By analyzing the transfer functions of LTI syst…
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting
Soon Hoe Lim, Yijin Wang, Annan Yu +4
Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic time series forecasting in latent spaces. However, the impa…
Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
Pu Ren, Rie Nakata, Maxime Lacour +9
Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer fro…
WaveCastNet: Rapid Wavefield Forecasting for Earthquake Early Warning via Deep Sequence to Sequence Learning
Dongwei Lyu, Rie Nakata, Pu Ren +4
We propose a new deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequen…
HOPE for a Robust Parameterization of Long-memory State Space Models
Annan Yu, Michael W. Mahoney, N. Benjamin Erichson
State-space models (SSMs) that utilize linear, time-invariant (LTI) systems are known for their effectiveness in learning long sequences. To achieve state-of-the-art performance, a…
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
Wuyang Chen, Jialin Song, Pu Ren +3
Recent years have witnessed the promise of coupling machine learning methods and physical domain-specific insights for solving scientific problems based on partial differential equ…
Robustifying State-space Models for Long Sequences via Approximate Diagonalization
Annan Yu, Arnur Nigmetov, Dmitriy Morozov +2
State-space models (SSMs) have recently emerged as a framework for learning long-range sequence tasks. An example is the structured state-space sequence (S4) layer, which uses the…