Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Computational Ocean Acoustics: Propagation Modeling and Applications teaches how to predict and use sound in the sea. You learn the physics of how sound propagates through a complex ocean — refraction, reflection and the sound channel — and the computational models (ray, mode and parabolic-equation methods) used to predict it. The course connects modelling to real applications: sonar, underwater communication, and monitoring marine life and the environment. You finish able to reason about modelling ocean acoustic propagation. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers computational ocean acoustics — modelling sound propagation in the ocean and its use in sensing, communication and marine monitoring.
📋 Course Objectives
1. Explain ocean sound-propagation physics.
2. Apply ray, mode and PE modelling methods.
3. Predict propagation in complex ocean conditions.
4. Connect models to sonar and communication.
5. Apply acoustics to marine monitoring.
👥 Who Should Enroll?
• Ocean and acoustic engineers
• Marine-science and sonar professionals
• Underwater-technology researchers
• Students of ocean acoustics
🚀 Key Learning Outcomes
• An understanding of ocean acoustics.
• A propagation-modelling perspective.
• A marine-acoustics foundation.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Computational Ocean Acoustics Propagation Modeling And Sonar Signal Processing Foundations
Apply mathematical concepts such as wave propagation and signal processing to analyze ocean acoustic phenomena • Develop computational models to simulate ocean acoustic propagation using numerical methods such as finite difference and finite element techniques • Evaluate the performance of different mathematical models in predicting sonar signal behavior in various ocean environments
Data Engineering, Preprocessing, and Feature Pipelines
Design data pipelines to preprocess and feature-engineer large datasets of ocean acoustic signals using techniques such as filtering and spectral analysis • Implement data quality control measures to handle missing or noisy data in ocean acoustic datasets • Configure data storage solutions to manage and retrieve large volumes of ocean acoustic data for analysis and modeling
Model Architecture, Algorithm Design, and Computational Ocean Acoustics Propagation Modeling And Sonar Signal Processing Methods
Develop deep learning models such as convolutional neural networks and recurrent neural networks to analyze ocean acoustic signals • Analyze the performance of different algorithmic techniques such as beamforming and matched filtering in sonar signal processing • Optimize model architectures to improve computational efficiency and accuracy in predicting ocean acoustic phenomena
Training, Hyperparameter Optimization, and Evaluation
Train machine learning models using large datasets of ocean acoustic signals and evaluate their performance using metrics such as accuracy and mean squared error • Implement hyperparameter tuning techniques such as grid search and random search to optimize model performance • Evaluate the robustness of trained models to various types of noise and interference in ocean acoustic environments
Deployment, MLOps, and Production Workflows
Deploy trained models in production environments using containerization techniques such as Docker • Develop monitoring and logging systems to track model performance and identify potential issues in real-time • Configure automated workflows to retrain models and update deployments in response to changes in ocean acoustic environments
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze potential biases in ocean acoustic datasets and develop strategies to mitigate their impact on model performance • Develop guidelines for responsible AI development and deployment in ocean acoustic applications • Evaluate the ethical implications of using AI in ocean acoustic applications such as sonar signal processing and marine mammal monitoring
Industry Integration, Business Applications, and Case Studies
Develop business cases for the adoption of AI in ocean acoustic applications such as offshore oil and gas exploration • Analyze the potential return on investment of AI-powered ocean acoustic solutions in various industries • Evaluate the feasibility of integrating AI-powered ocean acoustic solutions with existing industry workflows and systems
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
