Andreas Look

Title: Innovations of Andreas Look in Time-Series Predictions

Introduction

Andreas Look is a notable inventor based in Stuttgart, Germany. He has made significant contributions to the field of computer-controlled systems through his innovative patent. His work focuses on enhancing the accuracy of time-series predictions, which is crucial for various applications in technology and engineering.

Latest Patents

Andreas Look holds a patent for a computer-implemented method of training a model for making time-series predictions of a computer-controlled system. This method utilizes a stochastic differential equation (SDE) that includes both a drift component and a diffusion component. The drift component consists of a predefined part that incorporates domain knowledge and a trainable part. During the training process, the model predicts the values of SDE variables at a current time point based on their previous values, refining the model in the process. The predefined part of the drift component is evaluated to obtain a first drift, which is then combined with a second drift derived from the trainable part.

Career Highlights

Andreas Look is currently employed at Robert Bosch GmbH, a leading global supplier of technology and services. His role at the company allows him to apply his innovative ideas and contribute to advancements in computer-controlled systems. His expertise in time-series predictions has positioned him as a valuable asset in his field.

Collaborations

Andreas collaborates with talented professionals such as Melih Kandemir and Sebastian Gerwinn. Their combined efforts enhance the research and development initiatives at Robert Bosch GmbH, fostering an environment of innovation and creativity.

Conclusion

Andreas Look's contributions to the field of time-series predictions exemplify the impact of innovative thinking in technology. His patent reflects a significant advancement in the training of models for computer-controlled systems. Through his work, he continues to influence the future of predictive modeling in various applications.