[HTML][HTML] Invariance embedded physics-infused deep neural network-based sub-grid scale models for turbulent flows
R Bose, AM Roy - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
In this paper, we present two novel physics-infused neural network (NN) architectures that
satisfy various invariance conditions for constructing efficient and robust Deep Learning (DL) …
satisfy various invariance conditions for constructing efficient and robust Deep Learning (DL) …
Modeling and observations of North Atlantic cyclones: Implications for US Offshore wind energy
To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power
by 2030 and 110 GW by 2050, expansion of wind energy into US territorial waters prone to …
by 2030 and 110 GW by 2050, expansion of wind energy into US territorial waters prone to …
Accurate deep learning sub-grid scale models for large eddy simulations
R Bose, AM Roy - arXiv preprint arXiv:2307.10060, 2023 - arxiv.org
We present two families of sub-grid scale (SGS) turbulence models developed for large-
eddy simulation (LES) purposes. Their development required the formulation of physics …
eddy simulation (LES) purposes. Their development required the formulation of physics …
Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model
Accurate prediction of monthly runoff is critical for effective water resource management and
flood forecasting in river basins. In this study, we developed a hybrid deep learning (DL) …
flood forecasting in river basins. In this study, we developed a hybrid deep learning (DL) …
Objective satellite methods including AI algorithms reviewed for the tenth International workshop on tropical cyclones (IWTC-10)
QP Duong, A Wimmers, D Herndon, ZM Tan… - … Cyclone Research and …, 2023 - Elsevier
Here we explore the latest four years (2019–2022) of using satellite data to objectively
analyze tropical cyclones (TC) and issue recommendations for improved analysis. We first …
analyze tropical cyclones (TC) and issue recommendations for improved analysis. We first …
[HTML][HTML] Simulation of atlantic hurricane tracks and features: A coupled machine learning approach
Bose, R., A. Pintar, and E. Simiu, 2021: Forecasting the evolution of North Atlantic
hurricanes: A deep learning approach. NIST TN Tech. Note 2167, 36 pp., https://tsapps. nist …
hurricanes: A deep learning approach. NIST TN Tech. Note 2167, 36 pp., https://tsapps. nist …
yupi: Generation, tracking and analysis of trajectory data in Python
Studying trajectories is often a core task in several research fields. In environmental
modeling, trajectories are crucial to study fluid pollution, animal migrations, oil slick patterns …
modeling, trajectories are crucial to study fluid pollution, animal migrations, oil slick patterns …
Simulation of atlantic hurricane tracks and features: A deep learning approach
The objective of this paper is to employ machine learning (ML) and deep learning (DL)
techniques to obtain from input data (storm features) available in or derived from the …
techniques to obtain from input data (storm features) available in or derived from the …
Simple yet effective: a comparative study of statistical models for yearly hurricane forecasting
In this paper, we study the problem of forecasting the next year's number of Atlantic
hurricanes, which is relevant in many fields of applications such as land-use planning …
hurricanes, which is relevant in many fields of applications such as land-use planning …
[PDF][PDF] TROPICAL CYCLONE DETECTION IN COASTAL AREA USING RECURRENT ALL-PAIRS FIELD TRANSFORMER FEATURE EXTRACTION AND 3D-CNN …
N MALOTHU, DRV PRASAD, D KRISHNA - Journal of Theoretical and …, 2025 - jatit.org
High amplitude depending on the wind's top speed and the categorization of intensity are
both used in cyclone classification and prediction models. The whole spectrum of ideal …
both used in cyclone classification and prediction models. The whole spectrum of ideal …