Abstract: This disclosure provides techniques for providing or facilitating automatic generation or conversion to geometric designs based on system-level designs. An example method may include receiving a system model from a system modeling environment. The system model describes, represents, or reflects a typology of two or more system components. The topology describes connectivity and performance by the two or more system components. A processing device extracts the topology of the two or more system components from the system model. The topology is represented by two or more connected nodes. The topology describes connectivity and performance by two or more system components. The processing device generates design spaces for the two or more connected nodes. The processing device further generates geometric representations in the design spaces for each of the two or more connected nodes to form the geometric assembly based on the system model.

Abstract: The techniques discussed herein generally relate to a method and system for qualitative modeling of and reasoning about the behavior of spatio-temporal physical systems. In some embodiments, qualitative representations based on Tonti diagrams are used to describe lumped or distributed parameter systems. Using a topological structure of the physical system, some embodiments generate qualitative governing equations as symbolic constraints on qualitative state variables. The qualitative constraints may be used to produce a qualitative simulation of the physical system. The qualitative simulation may be used to guide conceptual design iterations with given design criteria, or for instantiation of quantitative or hybrid (qualitative and quantitative) models and simulations.

Abstract: This disclosure provides techniques for automatically generating a geometric representation based on a system-level model (e.g., a lumped parameter model, or LPM). The geometric representation may include a three-dimensional (3D) or cross-sectional shape, resulting from topology optimization within a design space automatically generated without human intervention. An example method may include identifying one or more constraints for each of two or more components of an LPM. One or more conditions are generated for the LPM. The one or more conditions are mapped to the one or more constraints. A processing device may generate a design space for a geometric representation to perform functions represented by the LPM. The geometric representation is subject to the generated one or more conditions. The processing device may then perform topology optimization of the geometric representation in the design space to generate an optimized geometry (e.g., a converged and/or final output).

Abstract: Methods and systems (e.g., for generating selecting a model-order reduction (MOR) technique and/or level of granularity for a MOR that provide at least one of: (a) flexible cost-accuracy tradeoffs, (b) a priori error bounds, and (c) a priori preservation of properties that provide physical fidelity) may include: providing governing equations for a physical system; defining quantities of interest (QoI) for the physical system; semi-discretizing the governing equations to obtain a full-order model (FOM) using a state-space representation; applying a MOR technique to the FOM to obtain a family of ROMs with different cost and accuracy tradeoffs, wherein each ROM approximates the FOM with respect to the QoI; and selecting one or more preferred ROMs from the family of ROMs based, at least in part, on the cost and accuracy tradeoffs.

Abstract: Methods and systems for modeling physical systems may use a hybrid approach for surrogate modeling that incorporates both modeling based on physical principles and fitting to data. For example, a method for developing a reduced-order models (ROM) of a physical system may comprise: defining a quantity of interest (QoI) for the physical system; defining a lumped-parameter surrogate (LPS) of the physical system based on physical principles; deriving a topology from the LPS; deriving a governing equation of the ROM from the topology, wherein the governing equation has unknown parameters; collecting data about the QoI of the physical system; and fitting the governing equation based on the data to derive values for the unknown parameters and yield the ROM, wherein the ROM approximates the QoI.

Abstract: The techniques discussed herein generally relate to a method and system for qualitative modeling of and reasoning about the behavior of spatio-temporal physical systems. In some embodiments, qualitative representations based on Tonti diagrams are used to describe lumped or distributed parameter systems. Using a topological structure of the physical system, some embodiments generate qualitative governing equations as symbolic constraints on qualitative state variables. The qualitative constraints may be used to produce a qualitative simulation of the physical system. The qualitative simulation may be used to guide conceptual design iterations with given design criteria, or for instantiation of quantitative or hybrid (qualitative and quantitative) models and simulations.