California Institute of Technology

Nvidia Corporation

Animashree Anandkumar

Chiara Daraio

Paul W Sternberg

Zhengyu Huang

John F Brady

Zongyi Li

Zhiwei Peng

Tingtao Zhou

Xuan Wan

Soon-Jo Chung

Yisong Yue

Yuke Zhu

Xichen Shi

Guanya Shi

Christopher Bongsoo Choy

Zichao Wang

Chaowei Xiao

Weili Nie

Bokui Shen

Michael O'Connell

Systems and methods for altering the geometry of a fluid channel to prevent upstream mobility of bacteria, using angled obstacles on the interior of the channel that among other things creates vortices that restrict the mobility. An optimized geometry can be realized by an artificial intelligence algorithm or similar methods based on performance of various configurations of obstacle parameters.

Anti-infection fluidic channel

One or more machine learning models (MLMs) may learn implicit 3D representations of geometry of an object and of dynamics of the object from performing an action on the object. Implicit neural representations may be used to reconstruct high-fidelity full geometry of the object and predict a flow-based dynamics field from one or more images, which may provide a partial view of the object. Correspondences between locations of an object may be learned based at least on distances between the locations on a surface corresponding to the object, such as geodesic distances. The distances may be incorporated into a contrastive learning loss function to train one or more MLMs to learn correspondences between locations of the object, such as a correspondence embedding field. The correspondences may be used to evaluate state changes when evaluating one or more actions that may be performed on the object.

Action-conditional implicit dynamics of deformable objects

A machine learning framework is described for performing generation of candidate molecules for, e.g., drug discovery or other applications. The framework utilizes a pre-trained encoder-decoder model to interface between representations of molecules and embeddings for those molecules in a latent space. A fusion module is located between the encoder and decoder and is used to fuse an embedding for an input molecule with embeddings for one or more exemplary molecules selected from a database that is constructed according to a design criteria. The fused embedding is decoded using the decoder to generate a candidate molecule. The fusion module is trained to reconstruct a nearest neighbor to the input molecule from the database based on the sample of exemplary molecules. An iterative approach may be used during inference to dynamically update the database to include newly generated candidate molecules.

System and method for retrieval-based controllable molecule generation

Systems and methods for learning based control in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training an adaptive controller. The method includes steps for receiving a set of training data that includes several training samples, wherein each training sample includes a state and a true uncertain effect value. The method includes steps for computing an uncertain effect value based on the state, computing a set of one or more losses based on the true uncertain effect value and the computed uncertain effect value, and updating the adaptive controller based on the computed set of losses.

Systems and methods for robust learning-based control during forward and landing flight under uncertain conditions

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Pasadena, CA, United States of America

Animashree Anandkumar