Bayesian optimization

Raymundo Arróyave discusses alloy discovery guided by both metallurgical knowledge and Bayesian optimization. The approach combines physical models, high-throughput CALPHAD calculations, microstructural information and experimental feedback to search chemical and processing spaces that are difficult to explore through data alone. Examples involving high-entropy and refractory alloys address multiple objectives and constraints, including feasibility, correlated properties, uncertainty and microstructural sensitivity. The seminar then connects these decision methods to the Autonomous Robotic Me

…model, language-model embeddings for Bayesian optimization, and how to measure the contribution of scientific intuition. Krause also explains the choice to pursue manufacturing alongside discovery, emphasizing production data and process know-how. The episode connects automated experimentation with the practical challenges of developing and scaling new alloys.

…this by approaching the problem as an optimization challenge in the context of developing brain models capable of characterizing in

During smooth pursuit, the oculomotor system needs to multi-task: eye velocity is adjusted to keep the moving stimulus on the fovea; in case of target blanking or loss of foveation, saccades are performed to catch up with the target. Mathematical models can be used to implement a quantitative description of underlying processes. Thus, models can provide insight by identifying parameters related to the physiological mechanisms (like pursuit gain or retinal delay).Here, we adapted a one-dimensional oculomotor model (Marti et al. 2008) to a two-dimensional pursuit task with target blanking (see f

Spinal cord stimulation is a promising therapy for the recovery after spinal cord injury and control of movement. However, the complex interaction between electrodes and neural reaction impedes more effective neuromodulation. Empirical knowledge and manual adjustment are playing dominant role for the tuning of neuromodulation parameters. The growth of electrode array’s complexity also brings new challenges to find optimal stimulating configurations. Developing a more efficient way to predict the stimulation results and explore the optimal therapy is necessary for the tuning of sensitive neural

New recording technologies and population analyses have made it increasingly conceivable to functionally dissect large-scale circuits in vivo, causally relating neural activity to behavior. Such direct testing typically relies on hypotheses formulated in advance of the experiment. However, preconceived hypotheses may be restricting our exploration of ever-larger neural systems and more complex behaviors. We propose that adaptive experimental designs give us the statistical efficiency to search large parameter spaces and the flexibility to change our models with evolving dynamics. We thus devel