ipower: Physics-Informed AI for Power Electronics
ipower helps engineering firms and electronics manufacturers develop power electronics with a focus on energy efficiency. It is designed for teams needing physics-informed machine learning to support digital twin applications.
At a glance
- Category
- Software Development
- Best for
- Engineering Companies, Electronics Manufacturers, Hardware Companies, Power Electronics Researchers
- Pricing
- Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- Power Electronic Circuit Design, Power Converter Control, Predictive Maintenance, Digital Twin Development
- Official website
- ipower.ai
ipower is a suite of physics-informed AI tools developed through a research project supported by Innovation Fund Denmark. It is designed to bridge the gap between academic AI research and industrial application in power electronics.
The tools are intended for engineering companies and hardware manufacturers in sectors such as automotive, aerospace, and general electronics. It supports the development of power electronic systems by combining physical models with machine learning.
Practical applications include the design of circuits, the optimization of power converters, and the use of diagnostics for system maintenance. The project also maintains an open-source data platform providing datasets for degradation and digital twin research.
Buyers should note that this is a research-driven project involving academic and industry partners; they should confirm the current availability and deployment process for the specific AI tools they wish to implement.
Key Features
Combines domain knowledge and physical models with machine learning to support accuracy and robustness.
Tools designed to assist in the design and development of power electronic circuits.
Supports diagnostics to help manage the maintenance of power electronics systems.
Provides a platform with degradation and physics-informed ML datasets for research and development.
Uses physics-informed machine learning for parameter estimation to support the creation of digital twins for power converters.
Provides AI-based tools for the control and operation of power converters.
Use Cases
Using physics-informed tools to support the design phase of power electronics.
Implementing AI-driven control algorithms to manage power converters.
Applying diagnostics and degradation datasets to identify potential failures.
Using the open-source data platform and PIML methods to create digital replicas of power converters.
Best For
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
ipower is a set of physics-informed AI tools designed for engineering companies and electronics manufacturers to help with the design, control, and maintenance of power electronics.
Yes, it provides an open-source data platform that includes degradation datasets and physics-informed ML datasets for digital twin applications.
Unlike typical data-driven approaches, ipower's tools couple deep neural networks with dynamic models of converters to address challenges related to training data and robustness.
Source category: Software Development
Source subcategory: Technical Computing
