Physics-Guided Neural Networks for Inversion-based Feedforward Control applied to Linear Motors

Ever-increasing throughput specifications in semiconductor manufacturing require operating high-precision mechatronics, such as linear motors, at higher accelerations. In turn this creates higher nonlinear parasitic forces that cannot be handled by industrial feedforward controllers. Motivated by this problem, in this paper we develop a general framework for inversion-based feedforward controller design using physics-guided neural networks (PGNNs). In contrast with black-box neural networks, the developed PGNNs embed prior physical knowledge in the input and hidden layers, which results in improved training convergence and learning of underlying physical laws. The PGNN inversion-based feedforward control framework is validated in simulation on an industrial linear motor, for which it achieves a mean average tracking error twenty times smaller than mass-acceleration feedforward in simulation.Comment: Submitted to 2021 IEEE Conference on Control Technology and Application

A synergistic future for AI and ecology

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence—they are critical for both persisting and thriving in an uncertain future

Physics-Guided Deep Learning for Dynamical Systems: A survey

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are interpretable but rely on rigid assumptions. And the direct numerical approximation is usually computationally intensive, requiring significant computational resources and expertise. While deep learning (DL) provides novel alternatives for efficiently recognizing complex patterns and emulating nonlinear dynamics, it does not necessarily obey the governing laws of physical systems, nor do they generalize well across different systems. Thus, the study of physics-guided DL emerged and has gained great progress. It aims to take the best from both physics-based modeling and state-of-the-art DL models to better solve scientific problems. In this paper, we provide a structured overview of existing methodologies of integrating prior physical knowledge or physics-based modeling into DL and discuss the emerging opportunities

The Application of Physics Informed Neural Networks to Compositional Modeling

Compositional modeling is essential when simulating processes involving significant changes in reservoir fluid composition. It is computationally expensive because we typically need to predict the states and properties of multicomponent fluid mixtures at several different points in space and time. To speed up this process, several researchers have used machine learning algorithms to train deep learning (DL) models on data from the rigorous phase-equilibrium (flash) calculations. However, one shortcoming of the DL models is that there is no explicit consideration for the governing physics. So, there is no guarantee that the model predictions will honor the thermodynamical constraints of phase equilibrium (Ihunde & Olorode, 2022). This work is the first attempt to incorporate thermodynamics constraints into the training of DL models to ensure that they yield two-phase flash predictions that honor the physical laws that govern phase equilibrium. A space-filling mixture design is used to generate one million different compositions at different pressures (Ihunde & Olorode, 2022). Stability analysis and flash calculations are performed on these compositions to obtain the corresponding phase compositions and vapor fraction (Ihunde & Olorode, 2022). Physics-informed neural network (PINN) and standard deep neural network (DNN) models were trained to predict two-phase flash results using the data from the actual phase-equilibrium calculations (Ihunde & Olorode, 2022). Considering the stochasticity of the deep learning optimization process, we used the seven-fold cross-validation to obtain reliable estimates of average model accuracy and variance (Ihunde & Olorode, 2022). Comparing the PINN and standard DNN models reveals that PINNs can incorporate physical constraints into DNNs without significantly lowering the model accuracy (Ihunde & Olorode, 2022). The evaluation of the model results with the test data shows that both PINN and standard DNN models yield coefficients of determination of ~97% (Ihunde & Olorode, 2022). However, the root-mean-square error of the physics-constraint errors in the PINN model is over 55% lower than that of the standard DNN model (Ihunde & Olorode, 2022). This indicates that PINNs significantly outperform DNNs in honoring the governing physics. Finally, we demonstrate the significance of honoring the governing physics by comparing the resulting phase envelopes obtained from overall compositions computed from the PINN, DNN, and linear regression model predictions (Ihunde & Olorode, 2022)