A method and system perform quantum-assisted finite elements-based, design optimization of an object to minimize a shape-specific quantity by manipulating the shape of the object using a processing unit, for example, a Quantum Processing Unit (QPU). As a result, a shape-specific quantity, such as an approximation of sound pressure at a specific position around an object, can be minimized by manipulating the object shape using the QPU.

A vibration noise reduction analysis method for automotive panel parts acquires optimal distribution of beads to be provided in an automotive panel part to reduce noise caused by vibration of the panel part. The vibration noise reduction analysis method is executed by a computer, and includes: an automotive body analysis model acquiring step; an analysis condition setting step; a single-bead arranged area setting step; a bead parameter distribution acquiring step; an equivalent radiated power (ERP) reacquisition step; a bead parameter distribution acquisition step on a minimum ERP in bead-arranged area; and an optimal bead distribution determination step. The bead parameter distribution acquiring step includes: a bead parameter selection step; a bead parameter distribution analysis model generation step; and a bead parameter design variable distribution analysis step.

A non-transitory computer-readable storage medium storing a n EMI calculation program that causes at least one computer to execute a process, the process includes inputting circuit information of a first circuit to a machine learning model; acquiring an EMI value at a certain frequency of the first circuit; selecting, based on an impedance characteristic of the first circuit and the EMI value at the certain frequency, first EMI information from a plurality of pieces of EMI information in each of which an impedance characteristic of each of a plurality of circuits is associated with EMI values at a plurality of frequencies of each of the plurality of circuits; and acquiring an EMI value at another frequency different from the certain frequency of the first circuit based on the EMI value at the certain frequency and the first EMI information.

The disclosure provides an electromagnetic device design system including: one or more processors of a machine; and computer-storage medium storing instructions, which when executed by the machine, cause the machine to perform operations for EM sensitivity analysis for an electromagnetic (EM) device. The operations include: initiating physical parameters of the EM device, wherein the EM device comprises multiple ports; performing EM simulation for the EM device at a pre-solution frequency using the finite-element method (FEM); applying single-size matrix Padé via Lanczos (MPVL) method in fast frequency sweep and performing EM simulation for the EM device under excitation at each port to obtain field solution of the EM device at frequencies in a frequency range; calculating S-parameters for the multiple ports of the EM device; and calculating derivatives of the S-parameters with respect to one of the physical parameters in the frequency range.

In example implementations described herein, there are systems and methods for computation of force and moment in the time domain for a physical system including one or more sensors, which can involve obtaining material properties and first modal properties of the physical system; generating a material property matrix from the material properties and second modal properties from the obtained modal properties; measuring, via the sensors, a set of motion responses of the physical system; obtaining first quantities based on the second modal properties and the material property matrix; calculating a first intermediate matrix from the second modal properties and the set of motion responses; recursively computing, for each time step during measurement of the response, a second intermediate matrix based on (1) the first quantities, (2) the second modal properties, (3) the first intermediate matrix, and (4) a previously computed second intermediate matrix from at least one previous time step; and calculating the force and the moment for each time step during the measurement of the set of motion responses based on the second intermediate matrix and the second modal properties.

Model elements of an executable model, representing a physical system, are partitioned into one or more linear portions and one or more nonlinear portions. Simulating behavior of the physical system, by executing the model, includes, for each of multiple simulation time intervals, for a first nonlinear portion, computing a correlation matrix characterizing noise associated with one or more ports of the model. A scattering matrix corresponds to a portion of the physical system represented by the first nonlinear portion without accounting for any noise within the portion of the physical system. The correlation matrix is derived from the scattering matrix based on noise within the portion of the physical system. Noise sources representing noise within the portion of the physical system are identified based on the correlation matrix. At least one characteristic of noise associated with each noise source is computed, and noise characteristics are output at selected ports.

A method for circuit analysis includes removing noise from an edge response of a circuit to produce a modified edge response and performing superposition of the modified edge response to generate a waveform. The method also includes adding the removed noise to the waveform to generate a noised waveform and generating, based on the noised waveform, an eye diagram for the circuit.

An information processing apparatus specifies a first pattern indicating a first layer included in first circuit data. The information processing apparatus generates, based on first wiring included in a second pattern indicating a second layer that is adjacent to the first layer and a slit included in the first pattern, second circuit data by changing the first pattern to a third pattern including second wiring corresponding to the first wiring. The information processing apparatus generates, based on the second circuit data, training data for machine learning.

A system for fabricating a semiconductor device includes one or more data processors configured to perform operations commanded by instructions stored in a non-transitory computer-readable medium. The instructions include receiving a layout including a plurality of elements, extracting parasitic values associated with the layout to generate a resistance and capacitance (RC) netlist, generating a modified RC netlist by adding element names of the elements of the layout to the RC netlist, performing a post-layout simulation on the modified RC netlist to determine whether the layout meets a predetermined specification, and fabricating a semiconductor device based on the layout when it is determined that the layout meets the predetermined specification. The RC netlist includes the extracted parasitic values. The modified RC netlist includes a netlist table storing the element names and the extracted parasitic values.

Apparatuses, systems, computer program products, and methods are disclosed for foundation model based fluid simulations. An apparatus includes a processor and a memory that stores code executable by the processor to receive a fluid foundation model that is pretrained on fluid data, deploy the received fluid foundation model into a downstream machine learning pipeline for a fluid dynamics application, reconfigure the fluid foundation model for the fluid dynamics application, and output results from the machine learning pipeline for the fluid dynamics application based on the reconfigured fluid foundation model.