Embodiments relate to a system for solving differential equations. The system is configured to receive problem packages corresponding to problems to be solved, each comprising at least a differential equation and a domain. A solver stores a plurality of nodes of the domain corresponding to a first time-step, and processes the nodes over a plurality of time-steps using a systolic array comprising hardware for solving the particular type of the differential equation. The systolic array processes each node to generate a node for a subsequent time-step using a sub-array comprising a plurality of branches, each branch comprising a respective set of arithmetic units arranged in accordance with a corresponding term of the discretized form of the differential equation, and an aggregator configured to aggregate the corresponding terms from each branch to generate node data for the subsequent time-step.

Embodiments relate to a system for solving differential equations. The system is configured to receive problem packages corresponding to problems to be solved, each comprising at least a differential equation and a domain. A solver stores a plurality of nodes of the domain corresponding to a first time-step, and processes the nodes over a plurality of time-steps using a systolic array comprising hardware for solving the particular type of the differential equation. The systolic array processes each node to generate a node for a subsequent time-step using a sub-array comprising a plurality of branches, each branch comprising a respective set of arithmetic units arranged in accordance with a corresponding term of the discretized form of the differential equation, and an aggregator configured to aggregate the corresponding terms from each branch to generate node data for the subsequent time-step.

Embodiments relate to a computing system for solving differential equations. The system is configured to receive problem packages corresponding to problems to be solved, each comprising at least a differential equation and a domain, and to select a solver of a plurality of solvers, based upon availability of each of the plurality of solvers. Each solver comprises a coordinator that partitions the domain of the problem into a plurality of sub-domains, and assigns each of the plurality of sub-domains to a differential equation accelerator (DEA) of a plurality of DEAs. Each DEA comprises at least two memory units, and processes the sub-domain data over a plurality of time-steps by passing the sub-domain data through a selected systolic array from one memory unit, and storing the processed sub-domain data in the other memory unit, and vice versa.

Embodiments relate to a computing system for solving differential equations. The system is configured to receive problem packages corresponding to problems to be solved, each comprising at least a differential equation and a domain, and to select a solver of a plurality of solvers, based upon availability of each of the plurality of solvers. Each solver comprises a coordinator that partitions the domain of the problem into a plurality of sub-domains, and assigns each of the plurality of sub-domains to a differential equation accelerator (DEA) of a plurality of DEAs. Each DEA comprises at least two memory units, and processes the sub-domain data over a plurality of time-steps by passing the sub-domain data through a selected systolic array from one memory unit, and storing the processed sub-domain data in the other memory unit, and vice versa.

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.

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.

A system, computer-readable medium, and a method to operate a vehicle in a manner that minimizes a cost to travel from an origin to a destination that includes finding the input to a flight control system that minimizes direct operating cost. The approach described herein employs an energy state approximation (ESA).

A system, computer-readable medium, and a method to operate a vehicle in a manner that minimizes a cost to travel from an origin to a destination that includes finding the input to a flight control system that minimizes direct operating cost. The approach described herein employs an energy state approximation (ESA).

A method of operating switched capacitor filter integration circuits by pre-charging a final filter capacitor thereof with the final full voltage gain value during a first subframe to obtain an enhanced signal to noise ratio without changes to the circuit or components thereof.

A method of operating switched capacitor filter integration circuits by pre-charging a final filter capacitor thereof with the final full voltage gain value during a first subframe to obtain an enhanced signal to noise ratio without changes to the circuit or components thereof.