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The phase-field method has become a useful tool for the simulation of classical metallurgical phase transformations as well as other phenomena related to materials science. The thermodynamic consistency that forms the basis of these…

Fulfilling the rich promise of rapid advances in time-domain astronomy is only possible through confronting our observations with physical models and extracting the parameters that best describe what we see. Here, we introduce {\sc…

The simulation and parameter optimization of coupled ocean circulation and ecosystem models in three space dimensions is one of the most challenging tasks in numerical climate research. Here we present a scientific toolkit that aims at…

Atmospheric and Oceanic Physics · Physics 2014-10-02 Jaroslaw Piwonski , Thomas Slawig

Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy…

Atmospheric and Oceanic Physics · Physics 2021-10-11 Hui Wan , Kai Zhang , Philip J. Rasch , Vincent E. Larson , Xubin Zeng , Shixuan Zhang , Ross Dixon

Hydroclimatic processes are characterized by heterogeneous spatiotemporal correlation structures and marginal distributions that can be continuous, mixed-type, discrete or even binary. Simulating exactly such processes can greatly improve…

Methodology · Statistics 2017-07-24 Simon Michael Papalexiou

Score distillation sampling~(SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a \textbf{single} image. It leverages pre-trained 2D diffusion models as teacher to guide the reconstruction…

Computer Vision and Pattern Recognition · Computer Science 2024-05-06 Xuanyu Yi , Zike Wu , Qingshan Xu , Pan Zhou , Joo-Hwee Lim , Hanwang Zhang

While computer science has seen remarkable advancements in foundation models, which remain underexplored in geoscience. Addressing this gap, we introduce a workflow to develop geophysical foundation models, including data preparation, model…

Geophysics · Physics 2023-12-18 Hanlin Sheng , Xinming Wu , Xu Si , Jintao Li , Sibo Zhang , Xudong Duan

Rainfall induced landslides and soil erosion are part of a complex system of multiple interacting processes, and both are capable of significantly affecting sediment budgets. These sediment mass movements also have the potential to…

Computational Engineering, Finance, and Science · Computer Science 2015-01-26 Claudio Bosco , Graham Sander

Accurate simulation of astronomical observations is a critical element for any modern analyses, be it to measure event rates, analyses population properties, validate or train pipelines, account for selection effects, or correct biases. We…

Imaging Earth structure or seismic sources from seismic data involves minimizing a target misfit function, and is commonly solved through gradient-based optimization. The adjoint-state method has been developed to compute the gradient…

Computational Physics · Physics 2021-04-28 Weiqiang Zhu , Kailai Xu , Eric Darve , Gregory C. Beroza

We present SaddleScape V1.0, a Python software package designed for the exploration and construction of solution landscapes in complex systems. The package implements the High-index Saddle Dynamics (HiSD) framework and its variants,…

Numerical Analysis · Mathematics 2026-01-06 Yuyang Liu , Hua Su , Zixiang Xiao , Lei Zhang , Jin Zhao

While instruction-based image editing is emerging, extending it to 360$^\circ$ panoramas introduces additional challenges. Existing methods often produce implausible results in both equirectangular projections (ERP) and perspective views.…

Computer Vision and Pattern Recognition · Computer Science 2025-12-24 Haoyi Zhong , Fang-Lue Zhang , Andrew Chalmers , Taehyun Rhee

The dominant paradigm in machine learning is to assess model performance based on average loss across all samples in some test set. This amounts to averaging performance geospatially across the Earth in weather and climate settings, failing…

Machine Learning · Computer Science 2025-10-31 Nick Masi , Randall Balestriero

Statistical models are an essential tool to model, forecast and understand the hydrological processes in watersheds. In particular, the understanding of time lags associated with the delay between rainfall occurrence and subsequent changes…

Traditional seismic processing workflows (SPW) are expensive, requiring over a year of human and computational effort. Deep learning (DL) based data-driven seismic workflows (DSPW) hold the potential to reduce these timelines to a few…

Machine Learning · Computer Science 2021-03-01 Zhaozhuo Xu , Aditya Desai , Menal Gupta , Anu Chandran , Antoine Vial-Aussavy , Anshumali Shrivastava

To address the steep learning curve and reliance on complex manual file editing and command-line operations in the traditional workflow of the mainstream open-source seismic wave simulation software SPECFEM, this paper proposes an…

Artificial Intelligence · Computer Science 2025-12-17 Yukun Ren , Siwei Yu , Kai Chen , Jianwei Ma

Geotechnical and seismic applications, ranging from site response analysis and HVSR simulations to dispersion curve modeling, increasingly depend on large, well-labeled datasets for robust model development. However, the scarcity of…

Geophysics · Physics 2025-12-16 Mersad Fathizadeh , Hosna Kianfar

Forward wavefield simulation is an important step in Full Waveform Inversion systems. Fast simulations are instrumental to get inversion result in reasonable time frames. Most of research and software aims towards utilizing costly computer…

Signal Processing · Electrical Eng. & Systems 2022-01-03 Emanuel Trabes , Silvana Spagnotto , Orlando Alvarez Pontoriero , Julio Daniel Dondo Gazzano , Carlos Federico Sosa Paez

Satellite imagery allows a plethora of applications ranging from weather forecasting to land surveying. The rapid development of computer vision systems could open new horizons to the utilization of satellite data due to the abundance of…

Computer Vision and Pattern Recognition · Computer Science 2020-07-10 Mohamed Abdelhack

Molecular simulation is a scientific tool dealing with challenges in material science and biology. This is reflected in a permanent development and enhancement of algorithms within scientific simulation packages. Here, we present…