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Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...
Energy Losses in Transformers01:21

Energy Losses in Transformers

In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the copper windings...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
Types Of Transformers01:16

Types Of Transformers

Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
Transformers in Distribution System01:27

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
The Ideal Transformer01:26

The Ideal Transformer

In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential component...

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Related Experiment Videos

Flexible Gravitational-Wave Parameter Estimation with Transformers.

Annalena Kofler1,2, Maximilian Dax1,3,4, Stephen R Green5

  • 1Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany.

Physical Review Letters
|July 10, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model, dingo-t1, offers flexible gravitational-wave data analysis. This transformer-based approach adapts to various settings, improving efficiency and enabling new tests of general relativity.

Related Experiment Videos

Area of Science:

  • Astrophysics
  • Gravitational-wave astronomy
  • Machine learning

Background:

  • Gravitational-wave data analysis requires efficient methods for noisy signals.
  • Current deep learning models lack flexibility for diverse analysis settings.
  • Adapting to variations is crucial for imperfect observations and specialized tests.

Purpose of the Study:

  • To develop a flexible deep learning model for gravitational-wave data analysis.
  • To enable adaptation to diverse analysis settings at inference time.
  • To improve efficiency and enable new tests of general relativity.

Main Methods:

  • Introduced a flexible transformer-based architecture named dingo-t1.
  • Developed a training strategy for adaptation to diverse analysis settings.
  • Applied the model to parameter estimation for binary black hole mergers.

Main Results:

  • Analyzed 48 binary black hole events from the LIGO-Virgo-KAGRA Observing Run.
  • Enabled systematic studies of detector and frequency configurations on inferred posteriors.
  • Improved median sample efficiency from 1.4% to 4.2% on real events.
  • Performed inspiral-merger-ringdown consistency tests probing general relativity.

Conclusions:

  • Demonstrated flexible and scalable inference for gravitational-wave data.
  • Provided a principled framework for handling missing or incomplete data.
  • Highlighted key capabilities for current and next-generation observatories.