Master Thesis Data Augmentation with Physics-Guided Diffusion Models for Probabilistic Safety Assess

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people's lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

With the emerging technologies like autonomous driving and x-by-wire systems, the vehicle's onboard power supply system, also known as the powernet, is subject to stringent safety requirements. Failure of the powernet leads immediately to the loss of all the safety-related functions such as braking, steering, autonomous driving features, etc. Among all the powernet components, special attention shall be paid on batteries due to their complex electrochemical nature. However, limited real-world data often hinders the development of reliable battery diagnostic models. To address this, this project explores the use of diffusion models for data augmentation, improving uncertainty quantification (UQ) and enabling probabilistic safety assessment. Diffusion models have demonstrated state-of-the-art performance in high-fidelity data generation, making them a promising approach for enhancing battery diagnostics with synthetic but realistic data. The research questions are: how can diffusion models be used to generate high quality synthetic battery data, how can we integrate diffusion models with physically informed priors for more realistic data generation using less data, what is the impact of data augmentation on failure probability estimation in battery diagnostics.

• As part of your Master thesis, you will assist us in conducting a comprehensive literature review on diffusion models and physics-guided generative models for data augmentation.
• You will implement a diffusion model (e.g., DDPM, DDIM, or conditional diffusion models) to generate battery data using PyTorch.
• In addition, you will compare diffusion models with GANs in terms of data fidelity, uncertainty quantification and robustness.
• Last but not least, you will validate the impact of synthetic data on battery diagnostics and probabilistic safety assessment.

Qualifications

• Education: Master studies in any field
• Experience and Knowledge: background in Machine Learning, Deep Learning, Physics-Informed AI, etc.; experience with PyTorch and deep generative models (GANs, VAEs or diffusion models); knowledge of battery diagnostics or battery systems is a plus
• Personality and Working Practice: you are a self-motivated and proactive person who is able to work independently
• Languages: very good communication skills in written and spoken German or English

Additional Information

Start: according to prior agreement
Duration: 3 - 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?
Zhiyi Xu (Functional Department)
+49 711 811 92252