The Path to Safe Fusion Energy through Deep Learning
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 2, Session 3: A Consideration of the Thought of David Novak
Tony DiTerlizzi

0   |  33:45

Tony DiTerlizzi
Tony DiTerlizzi
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 1, Session 3: Jewish-Christian Relations
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 2, Session 3: A Consideration of the Thought of David Novak

1   |  01:58:17

Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 2, Session 3: A Consideration of the Thought of David Novak
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 2, Session 3: A Consideration of the Thought of David Novak
Jefferson's Legal Common Book Symposium: Panel 1- Commonplacing: Jefferson’s Method and Purpose
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 1, Session 3: Jewish-Christian Relations

0   |  01:42:30

Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 1, Session 3: Jewish-Christian Relations
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 1, Session 3: Jewish-Christian Relations
Jefferson's Legal Commonplace Book Symposium: Keynote Address
Jefferson's Legal Common Book Symposium: Panel 1- Commonplacing: Jefferson’s Method and Purpose

0   |  01:33:29

Jefferson's Legal Common Book Symposium: Panel 1- Commonplacing: Jefferson’s Method and Purpose
Jefferson's Legal Common Book Symposium: Panel 1- Commonplacing: Jefferson’s Method and Purpose
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 1, Session 2: Natural Law
Jefferson's Legal Commonplace Book Symposium: Keynote Address

3   |  01:18:19

Jefferson's Legal Commonplace Book Symposium: Keynote Address
Jefferson's Legal Commonplace Book Symposium: Keynote Address
Sacramental Liberalism and Ragion di Stato - The Annual Herbert W. Vaughan Lecture on America’s Founding Principles
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 1, Session 2: Natural Law

1   |  01:45:44

Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 1, Session 2: Natural Law
Religious Particularity and Moral Universality - Faith, Reason, and Natural Law: Day 1, Session 2: Natural Law
Building Collective Leadership for Children: From Princeton to Teach for America to the Teach for All global network. A conversation with Wendy Kopp '89
Sacramental Liberalism and Ragion di Stato - The Annual Herbert W. Vaughan Lecture on America’s Founding Principles

8   |  01:28:59

Sacramental Liberalism and Ragion di Stato - The Annual Herbert W. Vaughan Lecture on America’s Founding Principles
Sacramental Liberalism and Ragion di Stato - The Annual Herbert W. Vaughan Lecture on America’s Founding Principles
The Path to Safe Fusion Energy through Deep Learning
Building Collective Leadership for Children: From Princeton to Teach for America to the Teach for All global network. A conversation with Wendy Kopp '89

6   |  54:27

Building Collective Leadership for Children: From Princeton to Teach for America to the Teach for All global network. A conversation with Wendy Kopp '89
Building Collective Leadership for Children: From Princeton to Teach for America to the Teach for All global network. A conversation with Wendy Kopp '89
Tony DiTerlizzi
The Path to Safe Fusion Energy through Deep Learning

2   |  04:46

The Path to Safe Fusion Energy through Deep Learning

San Diego Supercomputer Center Published on May 15, 2019 When the plasma inside a fusion reactor becomes unstable, there can be a release of energy that seriously damages the reactor. Key to preventing damage is predicting when these disruptions are about to happen. Professor William Tang of Princeton has successfully created a neural network that predicts these disruptions to a high level of accuracy, and was invited to give a talk at SDSC on his work. Tang's neural network is tuned…

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