相关论文: A Foundation Model for the Solar Dynamics Observat…
The solar wind, a continuous outflow of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is critical…
Time-dependent three-dimensional magnetohydrodynamics (MHD) simulation modules are implemented at the Joint Science Operation Center (JSOC) of Solar Dynamics Observatory (SDO). The modules regularly produce three-dimensional data of the…
We highlight the potential benefits of a synergistic use of SKAO and ESO facilities for galaxy evolution studies, focusing on the role that ESO spectroscopic surveys can play in supporting next-generation radio continuum and atomic hydrogen…
High resolution images at different wavelengths, spectrograms and magnetograms, representing different levels of the solar atmosphere obtained with Hinode have been combined to study the 3-dimensional structure of the small magnetic…
Predicting future dynamics is crucial for applications like autonomous driving and robotics, where understanding the environment is key. Existing pixel-level methods are computationally expensive and often focus on irrelevant details. To…
The solar magnetism is no more considered as a purely superficial phenomenon. The SoHO community has shown that the length of the solar cycle depends on the transition region between radiation and convection. Nevertheless, the internal…
Pre-trained deep learning models, known as foundation models, have become essential building blocks in machine learning domains such as natural language processing and image domains. This trend has extended to respiratory and heart sound…
Our knowledge of the Sun, its atmosphere, long term activity and space weather potential is severely limited by the lack of good observations of the polar and far-side regions. Observations from a polar vantage point would revolutionize our…
The mining of Virtual Observatories (VOs) is becoming a powerful new method for discovery in astronomy. Here we report on the development of SkyDOT (Sky Database for Objects in the Time domain), a new Virtual Observatory, which is dedicated…
Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality predictions. Earth Observation (EO) represents a quintessential…
Earth observation (EO) missions produce petabytes of multispectral imagery, increasingly analyzed using large Geospatial Foundation Models (GeoFMs). Alongside end-to-end adaptation, workflows make growing use of intermediate representations…
Vision foundation models (VFMs) trained on large-scale image datasets provide high-quality features that have significantly advanced 2D visual recognition. However, their potential in 3D scene segmentation remains largely untapped, despite…
The concept of surface-flux transport (SFT) is commonly used in evolving models of the large-scale solar surface magnetic field. These photospheric models are used to determine the large-scale structure of the overlying coronal magnetic…
FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building a ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO…
A primary challenge in developing synthetic spatial hearing systems, particularly underwater, is accurately modeling sound scattering. Biological organisms achieve 3D spatial hearing by exploiting sound scattering off their bodies to…
We introduce MOMO, the first multi-sensor foundation model for Mars remote sensing. MOMO uses model merge to integrate representations learned independently from three key Martian sensors (HiRISE, CTX, and THEMIS), spanning resolutions from…
The variation of solar irradiance is one of the natural forcing mechanisms of the terrestrial climate. Hence, the time-dependent solar irradiance is an important input parameter for climate modelling. The solar surface magnetic field is a…
In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the well-known orbitals wall restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density…
In recent years, computational power and data availability breakthroughs have revolutionized our ability to analyze complex physical systems through the inverse problem approach. Data-driven techniques like system identification and machine…
The sea surface temperature (SST), a key environmental parameter, is crucial to optimizing production planning, making its accurate prediction a vital research topic. However, the inherent nonlinearity of the marine dynamic system presents…