Abstract: Methods and systems of using a trained machine-learning model to perform root cause analysis on a manufacturing process. A pre-trained machine learning model is provided that is trained to predict measurements of non-faulty parts. The pre-trained model is trained on training measurement data regarding physical characteristics of manufactured parts as measured by a plurality of sensors at a plurality of manufacturing stations. With the trained model, then measurement data from the sensors is received regarding the manufactured part and the stations. This new set of measurement data is back propagated through the pre-trained model to determine a magnitude of absolute gradients of the new measurement data. The root cause is then identified based on this magnitude of absolute gradients. In other embodiments the root cause is identified based on losses determined between a set of predicted measurement data of a part using the model, and actual measurement data.

Abstract: A computer-implemented method includes receiving a combination recorded signals indicating current, voltage, vibrational, and sound information associated with a test device, generating a training data set utilizing the signals, wherein the training data set is sent to a machine learning model, and in response to meeting a convergence threshold of the machine learning model, outputting a trained model that outputs a prediction using the recorded signals from the combination. The prediction indicates a predicted signal characteristic. The method also includes comparing the prediction and signal associated with the test device to identify a prediction error associated with the device, and outputting a prediction analysis indicating information associated with at least the prediction error. The prediction analysis includes information indicative of a relationship between the device and its signals.

Abstract: A system includes a processor, wherein the processor is programmed to receive sound information and vibrational information from a device in a first environment, generate a training data set utilizing at least the vibrational information and a sound perception score associated with the corresponding sound of the vibrational information, wherein the training data set is fed into an un-trained machine learning model, in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model, receive real-time vibrational information from the device in a second environment, and based on the real-time vibrational information as an input to the trained machine learning model, output a real-time sound perception score indicating characteristics associated with sound emitted from the device.

Abstract: A system includes a processor in communication with one or more sensors, wherein the processor is programmed to receive data including one or more of real-time current information, real-time voltage information, or real-time vibrational information from a run-time device, wherein the run-time device is an actuator or electric dive, and utilize a trained machine learning model and the data as an input to the trained machine learning model, output a sound prediction associated with estimated sound emitted from the run-time device.

Abstract: A system includes a processor in communication with one or more sensors. The processor is programmed to receiving, from the one or more sensors, vibrational information and sound information associated with the vibrational information from a test device, generating a training data set utilizing at least the vibrational data and the sound information associated with the vibrational data, wherein the training data set is sent to a machine learning model configured to output sound predictions, receiving real-time vibrational data from a run-time device running an actuator or electric dive emitting the real-time vibrational data, and based on the machine learning model and the real-time vibrational data, output a sound prediction indicating a purported sound emitted from the run-time device.

Abstract: A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data from a sensor, generate a training data set utilizing the input data, wherein the training data set is created by creating one or more copies of the input data and adding noise to the one or more copies, send the training data set to a diffusion model, wherein the diffusion model is configured to reconstruct and purify the training data set by removing noise associated with the input data and reconstructing the one or more copies of the training data set to create a modified input data set, send the modified input data set to a fixed classifier, and output a classification associated with the input data in response to a majority vote of the classification obtained by the fixed classifier of the modified input data set.

Abstract: Materials, methods, and systems for biorthogonal ligation of hydrogel labels to crosslinked-natural polymer hydrogels are provided. A heterobifunctional linker includes a peptide-reactive activated functional group on the heterobifunctional linker, including an activated amine-reactive functional group, an activated thiol-reactive functional group and being reactive with a hydrogel comprising a crosslinked natural polymer. The heterobifunctional linker also includes a photocaged reactive group including a photocaged hydroxylamine, a photocaged alkoxyamine, a photocaged hydrazide, a photocaged amine, a photocaged tetrazine, or a photocaged alkyne-containing moiety. The peptide-reactive activated functional group does not include an azide.

Abstract: Methods and systems for training a machine learning model with measurement data captured during a manufacturing process. Measurement data regarding a physical characteristic of a plurality of manufactured parts is received as measured by a plurality of sensors at various manufacturing stations. A time-series dynamics machine learning model encodes the measurement data into a latent space having a plurality of nodes. Each node is associated with the measurement data of one of the manufactured parts and at one of the manufacturing stations. A batch of the measurement data can be built, the batch include a first node and a first plurality of nodes immediately connected to the first node via first edges, and measured in time earlier than the first node. A prediction machine learning model can predict measurements of a first of the manufactured parts based on the latent space of the batch of nodes.

Abstract: A machine learning (ML) method for predicting an electronic structure of an atomic system. The method includes receiving an atomic identifier and an atomic position for atoms in the atomic system; receiving a basis set including rules for forming atomic orbitals of the atomic system; forming the atomic orbitals of the atomic system; and predicting an electronic structure of the atomic system based on the atom identifier, the atom position for the atoms in the atomic system, and the atomic orbitals of the atomic system. The ML method is capable of extremely accurate and fast molecular property prediction. The ML can directly purpose basis dependent information to predict molecular electronic structure. The ML method, which may be referred to as an orbital mixer model, uses multi-layer perception (MLP) mixer layers within a simple, intuitive, and scalable architecture to achieve competitive Hamiltonian and molecular orbital energy and coefficient prediction accuracies.