ML4Earth - Physics-aware Machine Learning

Inspiration:

In this section, explain what inspired you to embark on the ML4Earth - Physics-aware Machine Learning project. Discuss the motivation behind using machine learning for earth sciences and hydrodynamics. What it does:

Describe the primary function or objective of the ML4Earth project. Explain how it contributes to flood modeling and earth sciences. How we built it:

Detail the methodology, tools, and technologies you used to develop the project. Explain the technical aspects of your approach. Challenges we ran into:

Discuss the obstacles, difficulties, or technical challenges you encountered during the development process. This can include issues related to data, modeling, or computational resources. Accomplishments that we're proud of:

Highlight the major achievements or milestones you reached with the ML4Earth project. Discuss any breakthroughs or noteworthy results. What we learned:

Share insights and lessons learned during the project's development. Discuss the knowledge and skills gained by the team. What's next for ML4Earth - Physics-aware Machine Learning:

Outline the future plans and goals for the ML4Earth project. Discuss potential enhancements, further research, or applications in the field of flood modelling and earth sciences.

Built With

• machine-learning
• python
• streamlit