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Found 13 papers, 4 papers with code
Reasoning-Enhanced Object-Centric Learning for Videos
Object-centric learning aims to break down complex visual scenes into more manageable object representations, enhancing the understanding and reasoning abilities of machine learning systems toward the physical world.
Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures
There is growing interest in using machine learning (ML) methods for structural metamodeling due to the substantial computational cost of traditional simulations.
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
To reduce the need for training data with simulated solutions, we pretrain neural operators on unlabeled PDE data using reconstruction-based proxy tasks.
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
In this work, we introduce Koopman VAE (KVAE), a new generative framework that is based on a novel design for the model prior, and that can be optimized for either regular and irregular training data.
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
Super-Resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation.
Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
In this paper, we present a PINN framework for seismic wave inversion in layered (1D) semi-infinite domain.
An Unsupervised Machine Learning Approach for Ground-Motion Spectra Clustering and Selection
Clustering analysis of sequence data continues to address many applications in engineering design, aided with the rapid growth of machine learning in applied science.
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
In this paper, we present a novel physics-informed neural network (PINN) model for seismic wave modeling in semi-infinite domain without the nedd of labeled data.
Physics-informed Deep Super-resolution for Spatiotemporal Data
High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales.
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena.
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs
Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines.
Encoding physics to learn reaction-diffusion processes
Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs).
Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting
To address this fundamental issue, this paper presents an incremental Bayesian tensor learning method for reconstruction of spatiotemporal missing data in SHM and forecasting of structural response.
