Our team has developed Physics-informed Neural Networks (PINN) models where physics is integrated into the neural network’s learning process – dramatically boosting the AI’s ability to produce accurate results. Described in our recent paper, PINN models are made to respect physics laws that force boundaries on the results and generate a realistic output.
Using sophisticated geospatial technology known as IBM PAIRS Geoscope, IBM researchers are shedding light on the environmental and societal impacts of the COVID-19 pandemic.
After uncovering a new Nasca Line formation with IBM Watson Machine Learning Accelerator on IBM Power Systems, Yamagata University will deploy IBM PAIRS in the hopes of further discoveries with AI.
In a new study in the journal Nature, an IBM Research-led collaboration describes an exciting breakthrough in a 140-year-old mystery in physics — one that enables us to unlock the physical characteristics of semiconductors in much greater detail and aid in the development of new and improved semiconductor materials.