Siemens Aktiengesellschaft

Biswadip Dey

Amit Chakraborty

Yaofeng Zhong

Anand A Kulkarni

Arindam Dasgupta

Georgia Olympia Brikis

Yingnan Cui

A computer-implemented system and method for synthesizing a controller for an under actuated robotic manipulator includes a machine learning based model having a plurality of neural network modules. Each module is configured to approximate a function related to an underactuated controller for a robotic manipulator. Parameters of each function are learned during training of the model using a loss function that satisfies one or more conditions including structure preservation, integrability and equilibrium assignment.

Synthesizing machine learning-based controllers for underactuated robotic manipulators

System and method for synthesizing a controller for a dynamical system includes a feeder neural network trained to estimate an ordinary differential equation (ODE) from time series training data (X) of a trajectory having embedded angular data and configured to learn dynamics of a physical system by encoding a generalization of a Hamiltonian representation of the dynamics using a constant external control term (u). A neural ODE solver receives the estimate of the ODE from the feeder neural network and synthesizes a controller to control the system to track a reference configuration.

Physics informed neural network for learning non-Euclidean dynamics in electro-mechanical systems for synthesizing energy-based controllers

A computer-implemented method for assessing material microstructure of a machine component involves obtaining a raw image of a section of the component captured via a microscope. The method further includes pre-processing the raw image to generate a ternary image defined by pixel data including three levels of intensities. The method further includes identifying, from the ternary image, phase boundaries delineating at a phase in a primary constituent material of the component. The method further includes determining a volume associated with the phase based on the identified phase boundaries. The proposed method may be utilized, for example, as an automated tool for assessing material degradation and for quality control of gas turbine engine components.

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Plainsboro, NJ, United States of America

Biswadip Dey