2 citations · 4 across the 5 of their papers we have counts for
6 papers
A Comprehensive Review of Phase-Averaged and Phase-Resolving Wave Models for Coastal Modeling Applications
Md Meftahul Ferdaus, Nathan Alton Cooper, Austin B. Schmidt +4
Predicting ocean wave behavior is challenging due to the difficulty in choosing suitable numerical models among many with varying capabilities. This review examines the development…
Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting
Edward Holmberg, Pujan Pokhrel, Maximilian Zoch +9
Physics-based solvers like HEC-RAS provide high-fidelity river forecasts but are too computationally intensive for on-the-fly decision-making during flood events. The central chall…
Physics-Informed Neural Network Surrogate Models for River Stage Prediction
Maximilian Zoch, Edward Holmberg, Pujan Pokhrel +8
This work investigates the feasibility of using Physics-Informed Neural Networks (PINNs) as surrogate models for river stage prediction, aiming to reduce computational cost while m…
A LightGBM based Forecasting of Dominant Wave Periods in Oceanic Waters
Pujan Pokhrel
In this paper, we propose a Light Gradient Boosting (LightGBM) to forecast dominant wave periods in oceanic waters. First, we use the data collected from CDIP buoys and apply vario…
Machine Learning in weakly nonlinear systems: A Case study on Significant wave heights
Pujan Pokhrel
This paper proposes a machine learning method based on the Extra Trees (ET) algorithm for forecasting Significant Wave Heights in oceanic waters. To derive multiple features from t…
Random Forest Classifier Based Prediction of Rogue waves on Deep Oceans
Pujan Pokhrel, Elias Ioup, Md Tamjidul Hoque +2
In this paper, we present a novel approach for the prediction of rogue waves in oceans using statistical machine learning methods. Since the ocean is composed of many wave systems,…