Akila Sampath successfully defends her PhD Proposal

Akila Sampath, iHARP Research Assistant, successfully defended her PhD Proposal on Wednesday, December 4, 2024. Join iHARP in congratulating Akila on her successful PhD proposal defense.

The Arctic region is undergoing rapid environmental changes, with sea ice loss being a prominent indicator. Accurate prediction of sea ice thickness and extent is crucial for understanding climate change impacts and informing policy decisions. Traditional climate models often struggle to capture the complex interactions between atmospheric, oceanic, and sea ice processes. In recent years, machine learning has emerged as a powerful tool for analyzing large datasets and making accurate predictions. However, machine learning models, while capable of capturing complex patterns, can sometimes produce unrealistic or physically implausible results. To address this limitation, we propose a novel approach that combines the power of machine learning with the rigor of physical principles. By integrating physics-informed techniques into machine learning models, we aim to develop more accurate and reliable predictions of sea ice conditions.

Physics-Informed Machine Learning (PIML) is a novel approach that blends traditional scientific models with the pattern-recognition capabilities of machine learning to improve predictions of sea ice thickness. By leveraging both data-driven and physics-based knowledge, PIML offers more accurate and reliable predictions, even in data-scarce environments. This approach is particularly well-suited for studying complex systems like the Arctic sea ice. Our research introduces Physics-Encoded Neural Networks (PeNNs) to predict snow density. PeNNs embed physical laws directly into the neural network architecture, enabling the estimation of hidden physical properties from observable data. This makes PeNNs highly effective for tracking snow and ice conditions with precision. Additionally, we developed a physics-guided model (PGM) to investigate the causal link between sea ice loss and increased turbulence in the Beaufort Gyre through wind current. By incorporating physical constraints, the PGM ensures that predictions align with known scientific principles, aiding in the understanding of these causal relationships. Our proposed models offer a more comprehensive approach to studying Arctic sea ice. By integrating physics into machine learning, we aim to create tools that are both accurate and interpretable. This research provides valuable insights into Arctic climate change and demonstrates how physics can enhance the capabilities of machine learning models, leading to a deeper understanding of the impacts of sea ice loss.