G06F111/08

Optical flow based forecasting of binding states in molecular dynamic simulations

A computer-implemented method for executing a computation task in a molecular dynamic simulation includes identifying a bonding target on a ligand; constructing a protein structure; rendering an image of the ligand; subsampling data pertaining to the constructed protein structure and the image of the ligand at a particular frequency; rendering a two-dimensional image of the constructed protein structure relative to the ligand from a plurality of viewpoints; computing optical flows of the protein structure relative to the ligand based on the two-dimensional image; analyzing the optical flows to determine a displacement of atoms; simulating a binding state outcome of the protein structure relative to the ligand for each of the plurality of viewpoints; and predicting a probability of the protein structure binding with the ligand, based on the predicted binding state outcome for each of the plurality of viewpoints.

Building performance assessment system and method

A virtual data acquisition component holds a physics-based, building energy model having a plurality of predicted building performance metrics produced through a simulation of expected building performance. A physical data acquisition component obtains a plurality of trended building performance metrics during the operation of a building. An integrated interface having an analytics platform receives the plurality of predicted building performance metrics from the virtual data acquisition component and the plurality of trended building performance metrics from the physical data acquisition component and produces an analytic data product. The analytic data product can be compared to the predicted building performance metrics or the trended building performance metrics.

Deterministic sampling of autonomous vehicle simulation variables at runtime

Embodiments of a variable system for simulating the operation of an autonomous system, such as an autonomous vehicle, are disclosed. A layered approach for defining variables can allow changing the specification of those variables under the rules of override and refinement, while leaving the software components that query those variables at runtime unaffected. The variable system can facilitate, among others, deterministic sampling of variables, simulation variations, noise injection, and realistic message timing. These applications can make the simulator more expressive and more powerful by virtue of being able to test the same scenario under many different conditions. As a result, more exhaustive testing can be performed without requiring user intervention and without having to change the individual software components of the simulator.

Simulating adaptive experiments for feasibility analysis
12488165 · 2025-12-02 · ·

A variation testing system environment for simulating adaptive experiments of objects is disclosed. An experiment system conducts one or more simulations of an adaptive experiment that includes a plurality of variants of an object. Simulation results based on the one or more simulations are generated that are indicative of at least an estimated amount of time to conduct a real-world adaptive experiment based on the one or more simulations.

Wafer defect prediction device and operating method thereof

A method of predicting wafer defect information includes estimating a distribution with respect to defect occurrence time data, the defect occurrence time data including information about a time associated with a wafer defect occurrence, distinguishing a defect distribution type according to a result of the estimating the distribution, and outputting wafer defect information predicted according to the distinguished defect distribution type.

System for universal hardware-neural network architecture search (co-design)

An architecture search system evaluates a search space of neural network and hardware architectures with a plurality of candidate controllers. Each controller attempts to identify an optimized architecture using a different optimization algorithm. To identify a controller for the search space, the architecture search system samples subspaces of the search space having a portion of the neural network search space and a portion of the hardware search space. For each subspace, candidate controllers are scored with respect to the optimized design determined by the respective candidate controllers. Using the scores for the various candidate controllers across the sampled subspaces, a controller is selected to optimize the overall network architecture search space.