Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Pu Ren
Learning earthquake ground motions via conditional generative modeling
Abstract Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources.
Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning - Nature Communications
Abstract We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time.
WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning
[Submitted on 30 May 2024] Title:WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning View a PDF of the paper titled WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning, by Dongwei Lyu and 5 other authors View PDF HTML (experimental) Abstract:Large earthquakes can be destructive and quickly wreak havoc on a landscape.
An unsupervised machine learning approach for ground‐motion spectra clustering and selection
1 INTRODUCTION Feature engineering is critical in representation learning and is used in many domains with applications like stock price forecasting, neurological diagnosis, and acoustic scene classification.1-3 Unsupervised machine learning (ML) is a tool in artificial intelligence used to deduce the natural structure within a dataset lacking target labels.
Encoding physics to learn reaction–diffusion processes - Nature Machine Intelligence
Abstract Modelling complex spatiotemporal dynamical systems, such as reaction–diffusion processes, which can be found in many fundamental dynamical effects in various disciplines, has largely relied on finding the underlying partial differential equations (PDEs). However, predicting the evolution of these systems remains a challenging task for many cases owing to insufficient prior knowledge and a lack of explicit PDE formulation for describing the nonlinear process of the system variables.
[2106.04781] Encoding physics to learn reaction-diffusion processes
[Submitted on 9 Jun 2021 ( v1 ), last revised 22 May 2023 (this version, v2)] Title:Encoding physics to learn reaction-diffusion processes Download a PDF of the paper titled Encoding physics to learn reaction-diffusion processes, by Chengping Rao and 5 other authors Download PDF Abstract: Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs).
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Pu
Contact Pu, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack