Maziar Raissi

PUNCH: Physics-informed uncertainty-aware network for coronary hemodynamics

Sukirt Thakur, Marcus Roper, Yang Zhou, Dmitry Isaev et autres

More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions are identified in 70% of patients evaluated for ischemic heart disease. Up to half of these patients …

us (code pays fourni par la source)

Exploring Multiple Timescale Dynamics Using Geometric Singular Perturbation-Informed Neural Networks (GSPINNs)

Hamid Mofidi, Maziar Raissi, Wuyue Yang

Abstract. Multiple timescale systems have long been a subject of extensive study, with geometric singular perturbation theory (GSPT) emerging as a common tool for analyzing such systems. In this work, we present a comprehensive study of ordinary differential equations in the form …

cn, es, us (code pays fourni par la source)

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations

Satish Chandran, Maedeh Makki, Maziar Raissi, Adrien Grenier et autres

We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving differential equations. Rather than enforcing governing equations and boundary conditions through residual-based loss terms, NewPINNs integrates the solver directly into the training loop and defines …

Data-Efficient Physics-Informed Learning to Model Synchro-Waveform Dynamics of Grid-Integrated Inverter-Based Resources

Shivanshu Tripathi, Hossein Mohsenzadeh Yazdi, Maziar Raissi, Hamed Mohsenian‐Rad

Inverter-based resources (IBRs) exhibit fast transient dynamics during network disturbances, which often cannot be properly captured by phasor and SCADA measurements. This shortcoming has recently been addressed with the advent of waveform measurement units (WMUs), which provide high-resolution, time-synchronized raw voltage and …

PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics

Sukirt Thakur, Marcus Roper, Yang Zhou, Dmitry Isaev et autres

More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions are identified in 70% of patients evaluated for ischemic heart disease. Up to half of these patients …

us (code pays fourni par la source)

Physics-Informed Machine Learning for Smart Additive Manufacturing

Rahul Sharma, Maziar Raissi, Y.B. Guo

Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML’s “black box” nature has presented a challenge to interpreting its outcomes. On the other hand, governing physical laws are …

us (code pays fourni par la source)