Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

Many computer vision techniques infer properties of our physical world from images.
Although images are formed through the physics of light and mechanics, computer vision …

Symbolic regression is a powerful technique to discover analytic equations that describe
data, which can lead to explainable models and the ability to predict unseen data. In …

Synthetic data are becoming a critical tool for building artificially intelligent systems.
Simulators provide a way of generating data systematically and at scale. These data can …

The exploding usage of physical object properties has greatly facilitated real-time
applications such as robotics to perceive exactly as it appears in existence. Changes in the …

Neural networks have recently been used to analyze diverse physical systems and to
identify the underlying dynamics. While existing methods achieve impressive results, they …

The phenomenal success of physics in explaining nature and engineering machines is
predicated on low dimensional deterministic models that accurately describe a wide range …

While humans effortlessly discern intrinsic dynamics and adapt to new scenarios, modern AI
systems often struggle. Current methods for visual grounding of dynamics either use pure …

Unstable free-flight rigid body motion, consisting of 3D translational motion and large
angular rates about all axes and orientations outside of typical flight envelopes, is a complex …

For centuries, humans have discovered the physical laws that underpin our world. What if
the next Einstein or Newton is not a human, but a machine? Machines that are physics …