System and method for hybrid-model-based micro-assembly control with the aid of a digital computer

A system for hybrid-model-based micro-assembly control utilizing loss function selection with the aid of a digital computer, comprising:

• at least one processor configured to:
• obtain one or more parameters of a closed-loop system for positioning one or more of a plurality of micro-objects, the system comprising a plurality of programmable electrodes, the electrodes configured to induce movements of the micro-objects when the micro-objects are proximate to electrodes upon actuations of the electrodes, each of the actuations comprising a generation of one or more electric potentials by one or more of the electrodes;
• obtain recorded data comprising input states and output states associated with previous movements of one or more of the plurality of the micro-objects due to earlier ones of the actuations;
• build a hybrid model for predicting one or more positions of at least one of the micro-objects following one or more of the actuations, the hybrid model comprising a plurality of models, using the recorded data via gradient boosting, comprising:
• define a number of the plurality of models to be included into the hybrid model based on user input, wherein the plurality of models comprises a physics-based model and remaining models;
• build the physics-based model as one of the plurality of models using one or more of the parameters;
• make, using the physics-based model, a prediction regarding one of the positions of one of the micro-objects using one of the input states;
• select a loss function;
• use the loss function to determine a residual between the physics-based model prediction and one of the output states associated with the one input state used for the physics-based model prediction;
• build one of the remaining models using the residual associated with the physics-based model;
• and
• process one or more of the remaining models in the hybrid model until a number of the models built equals the defined number, wherein at least some of the remaining models are machine-learning models, comprising:
• make, using one of the remaining models, a prediction regarding one of the positions of one of the micro-objects using one of the input states;
• select a further loss function for that remaining model;
• use the further loss function to determine a residual between the remaining model's prediction and one of the output states associated with the one input state used for the remaining model's prediction;
• and
• build another one of the remaining models using the residual for the remaining model's prediction;
• and
• control the closed-loop system to perform one or more of the actuations based on the hybrid model.

A system for hybrid-model-based micro-assembly control utilizing stochastic-model-based loss function selection with the aid of a digital computer, comprising:

• at least one processor configured to:
• obtain one or more parameters of a closed-loop system for positioning one or more of a plurality of micro-objects, the system comprising a plurality of programmable electrodes, the electrodes configured to induce movements of the micro-objects when the micro-objects are proximate to electrodes upon actuations of the electrodes, each of the actuations comprising a generation of one or more electric potentials by one or more of the electrodes;
• obtain recorded data comprising input states and output states associated with previous movements of one or more of the plurality of the micro-objects due to earlier ones of the actuations;
• build a hybrid model for predicting a position of at least one of the micro-objects following one of the actuations, the hybrid model comprising a plurality of models, using the recorded data via gradient boosting, comprising:
• define a number of the plurality of models to be included into the hybrid model based on user input, wherein the plurality of models comprises a physics-based model and remaining models;
• build the physics-based model as one of the plurality of models using one or more of the parameters;
• make, using the physics-based model, a prediction regarding one of the positions of one of the micro-objects using one of the input states;
• select a loss function based on a stochastic model associated with the physics-based model prediction;
• use the loss function to determine a residual between the physics-based model prediction and one of the output states associated with the one input state used for the physics-based model prediction;
• building one of the remaining models using the residual associated with the physics-based model;
• and
• process one or more of the remaining models in the hybrid model until a number of the models built equals the defined number, wherein at least some of the remaining models are machine-learning models, comprising:
• make, using one of the remaining models, a prediction regarding one of the positions of one of the micro-objects using one of the input states;
• select a further loss function for that remaining model based on a further stochastic model associated with the remaining model's prediction;
• use the further loss function to determine a residual between the remaining model's prediction and one of the output states associated with the one input state used for the remaining model's prediction;
• and
• build another one of the remaining models using the residual for the remaining model's prediction;
• and
• control the closed-loop system to perform one or more of the actuations based on the hybrid model.