PhysicsX

PhysicsX is an AI platform specialized in accelerating physical simulations and engineering processes through deep learning. The tool enables companies to drastically shorten complex design cycles by utilizing AI-driven predictions instead of relying solely on computationally expensive traditional numerical methods like CFD or FEA. It combines data from simulations and real-world tests to create precise digital models.

The platform is built on a proprietary stack optimized to integrate physical constraints and laws into the AI learning process. This ensures higher reliability compared to purely data-driven black-box models. Users can conduct complex parameter studies in seconds, tasks that would typically require hours or days using traditional solvers.

PhysicsX is primarily used in industries with demanding requirements for aerodynamics, thermodynamics, and material sciences, such as aerospace, automotive engineering, and sustainable energy technology. The platform acts as a supplementary tool, empowering engineers to optimize prototypes faster and identify potential design flaws in early development stages.

• AI Acceleration of Physical Solvers — PhysicsX integrates deep learning models to reduce the computation time of traditional physical simulation tools by several orders of magnitude. This enables real-time optimization of components and systems.
• Physics-Informed Learning — The models are designed to incorporate physical laws as constraints, ensuring the physical consistency and accuracy of predictions. This minimizes the risk of generating mathematically plausible but physically impossible results.
• Data Fusion from Simulation and Real-World Tests — The system processes heterogeneous data sources, including numerical simulation results and real-world physical test measurements. This fusion enhances the accuracy of the operational digital twin.

The platform is aimed at engineers, physicists, and R&D departments in industrial sectors dealing with complex numerical simulations. It is particularly relevant for teams working under tight deadlines to develop highly optimized physical designs.

An aerodynamics team uses PhysicsX to optimize vehicle body airflow. Instead of simulating every design iteration from scratch, the team trains a PhysicsX model using existing CFD data. Within minutes, the AI predicts the drag coefficient for thousands of new shape variants. This leads to a significantly more efficient design process and enables the targeted selection of the most promising body concepts for final validation.

• The platform is aimed at engineers, physicists, and R&D departments in industrial sectors dealing with complex numerical simulations. It is particularly relevant for teams working under tight deadlines to develop highly optimized physical designs.
• An aerodynamics team uses PhysicsX to optimize vehicle body airflow.
• AI Acceleration of Physical Solvers: PhysicsX integrates deep learning models to reduce the computation time of traditional physical simulation tools by several orders of magnitude. This enables real-time optimization of components and systems.
• Physics-Informed Learning: The models are designed to incorporate physical laws as constraints, ensuring the physical consistency and accuracy of predictions. This minimizes the risk of generating mathematically plausible but physically impossible results.
• Data Fusion from Simulation and Real-World Tests: The system processes heterogeneous data sources, including numerical simulation results and real-world physical test measurements. This fusion enhances the accuracy of the operational digital twin.

The tool is not suitable for simple, non-physical data analysis or general AI tasks such as text generation. It should not be used if there is no high-quality simulation or measurement data available to serve as a foundation for training.

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