Gradient propagation for simulation of magnetic domain wall motion

Transforming simulations into insights for advanced data generation and modeling in physics.

Innovating Data Generation and Modeling

We specialize in simulating domain wall motion and enhancing AI's understanding of physics through advanced data generation and model training techniques.

A computer screen displaying a coding interface with Python code related to machine learning. The code imports libraries like sklearn and deals with model metrics such as precision and recall. A classification report is shown along with a section titled 'Different meta model trained' listing various models like DT, RF, LR, and XGB. Below, there is code for tuning an XGB model using GridSearchCV.
A computer screen displaying a coding interface with Python code related to machine learning. The code imports libraries like sklearn and deals with model metrics such as precision and recall. A classification report is shown along with a section titled 'Different meta model trained' listing various models like DT, RF, LR, and XGB. Below, there is code for tuning an XGB model using GridSearchCV.

Advanced Data Solutions

Transforming simulations into actionable insights through innovative data generation and modeling techniques.

Simulation Insights

Utilizing mumax3 for precise domain wall motion simulations and energy density gradient annotations.

A conference room setting with several laptops on a large table, each being used by a person. A large screen displays a blue interface with the text 'Generate ad creatives from any website with AI'. A stainless steel water bottle and a conference phone are also visible on the table.
A conference room setting with several laptops on a large table, each being used by a person. A large screen displays a blue interface with the text 'Generate ad creatives from any website with AI'. A stainless steel water bottle and a conference phone are also visible on the table.
Model Training

Fine-tuning AI with physical parameters to enhance text-physics mapping and mathematical reasoning.

A partial simulation or model of an airplane cockpit positioned in a room. The setup includes two seats and various control panels. The surrounding area contains tools, a wooden crate, electronic devices, and a bicycle trainer.
A partial simulation or model of an airplane cockpit positioned in a room. The setup includes two seats and various control panels. The surrounding area contains tools, a wooden crate, electronic devices, and a bicycle trainer.

Domain Wall

Simulating domain wall motion using advanced data generation techniques.

A realistic human-like mannequin or training dummy is positioned against a blurred green natural background. The mannequin has a stern, intense expression on its face, with detailed facial features including furrowed brows and deep-set eyes. It appears to be made of a smooth, solid material.
A realistic human-like mannequin or training dummy is positioned against a blurred green natural background. The mannequin has a stern, intense expression on its face, with detailed facial features including furrowed brows and deep-set eyes. It appears to be made of a smooth, solid material.
Model Training

Fine-tuning simulations into text descriptions enhances understanding of physical parameters and promotes effective communication of complex physics concepts.

Several black boxes labeled 'Training Box' are stacked on a grassy surface. The top box features illustrations of a kettlebell and a dumbbell.
Several black boxes labeled 'Training Box' are stacked on a grassy surface. The top box features illustrations of a kettlebell and a dumbbell.
Validation Process

Comparing AI predictions with ground-truth simulations ensures accuracy and robustness in modeling domain wall dynamics under various conditions.

Simulation

Transforming data into meaningful text descriptions.

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